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New Technology and
Automation in Freight
Transport and Handling
Systems
Future of Mobility: Evidence Review
Foresight, Government Office for Science
New Technology and Automation in Freight Transport and Handling Systems
New Technology and Automation
in Freight Transport and Handling
Systems
Daniela Paddeu, Thomas Calvert, Ben Clark, Graham Parkhurst
University of the West of England, Bristol
February 2019
This report has an information cut-off date of June 2018.
This review has been commissioned as part of the UK
government’s Foresight Future of Mobility project. The
views expressed are those of the author and do not
represent those of any government or organisation.
This document is not a statement of government policy.
New Technology and Automation in Freight Transport and Handling Systems
1
1. Contents
1. Contents ............................................................................................................................ 1
2. Executive summary ......................................................................................................... 3
a. Key technologies .......................................................................................................... 3
b. Drivers and constraints on innovation .......................................................................... 4
c. Implications for policy and research ............................................................................. 4
3. Introduction ...................................................................................................................... 7
4. Methodology ..................................................................................................................... 8
5. Literature review .............................................................................................................. 9
a. Automated loading systems at ports and depots ......................................................... 9
b. Self-driving or remote-controlled units and stacking equipment................................ 15
c. Freight transport of the future ..................................................................................... 16
6. Disruptive business models ......................................................................................... 35
7. Stakeholder requirements ............................................................................................ 39
a. The carrier .................................................................................................................. 39
b. The shipper ................................................................................................................. 39
c. The truck driver ........................................................................................................... 39
d. The regulator .............................................................................................................. 40
e. The systems provider ................................................................................................. 40
f. The policymaker ............................................................................................................. 40
g. Driver training service providers ................................................................................. 41
h. Service providers of business support and education systems................................. 41
i. System providers for platooning coordination ............................................................... 41
8. Enablers and barriers .................................................................................................... 41
a. Enablers ...................................................................................................................... 43
b. Barriers ....................................................................................................................... 46
9. Building the future ......................................................................................................... 47
a. Implications: a macroeconomic perspective .............................................................. 48
New Technology and Automation in Freight Transport and Handling Systems
2
b. Implications for sector employment and inequalities ................................................. 48
c. Implications for the trucking industry and logistics service providers ........................ 52
d. Implications for the environment ................................................................................ 53
e. What the ‘freight future’ might look like in the UK ...................................................... 54
10. Recommendations ....................................................................................................... 57
11. References .................................................................................................................... 59
New Technology and Automation in Freight Transport and Handling Systems
3
2. Executive summary
This report presents a state-of-the-art review of new technologies and automation in
freight transport and handling. The review identifies:
• key emerging technologies, how are they being applied in the UK, and
examples of (international) best practice
• the drivers of, and constraints on, innovation in the UK freight sector
• implications for policies and research
a. Key technologies
With respect to long-haul road transportation, the strongest prospects in the short
to medium term relate to platooning
1
of heavy goods vehicles (HGVs). The
technology is already mature, and the next step will be to develop, through trials, the
appropriate regulatory frameworks and operating practices to enable the safe
platooning of HGVs on public highways. Over the longer term, there is potential for
autonomous electric and connected vehicles to be utilised in the freight sector.
In relation to long-haul rail transportation, on the European continent, high-speed rail
lines are utilised already as ‘rolling motorways’ through which freight containers are
transported for the longest part of the journey. Various transhipment systems have
been developed to automate the transfer of cargo containers between road and rail
(e.g. CargoBeamer, the Lohr Railway System and InnovaTrain). Such systems could
be readily deployed on new sections of the UK rail network (i.e. High Speed lines 1
and 2; HS1 and HS2), since these will meet the required design standards.
Emerging solutions for last-mile deliveries include autonomous vehicles (AVs),
drones and 3D printing. These can be combined in ‘urban freight systems’ with local
cross-docking centres for receiving and collecting goods: e.g. consolidation centres,
pick-up points and pack stations. With the establishment of the necessary regulatory
frameworks, there is potential for such innovative solutions to reduce road freight
movements in urban areas. Figure 1 summarises some innovative technologies,
splitting them by where they operate, e.g. air or road. As transport is a system,
changes in one part will also impact other modes.
1
‘Platooning’ is the grouping of vehicles in operation on a highway in such a way that their control
systems are temporarily linked, which enables the space between the vehicles to be reduced. In turn,
greater proximity results in more efficient use of roadspace and increased energy efficiency for
second and subsequent vehicles in a platoon. The lead vehicle also has an aerodynamic advantage,
and lower fuel consumption. More generally it refers both to the connection of the vehicles through a
data link, but also limited or partial automation for following vehicles as the control systems are linked.
New Technology and Automation in Freight Transport and Handling Systems
4
b. Drivers and constraints on innovation
As well as being a necessary component of an internationally-leading economy fit for
the information age, the provision of nationwide superfast/gigabit broadband and
high-speed mobile networks will also be fundamental to stimulating innovation in the
freight sector. Such high-capacity data transmission networks may help unlock the
potential for:
• increased capacity to transmit logistics data between freight providers (and to
customers), to improve efficiency in freight operations
• the operation of automated drones for last-mile deliveries
Views differ as to whether future self-driving freight vehicles will need to be
connected and if so how. For safety reasons, some operation without active real
time remote data feeds, is likely to have to be designed into systems. Will vehicle-to-
vehicle, vehicle-to-infrastructure or vehicle-to-cloud technologies be necessary? If
such connectivity becomes necessary, then any such connections will also need to
ensure the safety of the vehicle and its data.
However, compelling and reliable evidence of supply chain efficiency gains and cost
reductions is necessary to incentivise private sector stakeholders to invest in these
new technologies.
Conversely, the review points towards a number of factors that could stifle innovation
in the freight sector, including:
• if the autonomous operation model pursued requires complementary infrastructure,
then this will need to be in place. However, this is unnecessary for many current
operating models
• a perception of limited added value and comparatively poor economies of scale
associated with new technologies, which are likely to require high capital
investments in the early development stages
• a lack of compelling evidence that new technologies are safe, reliable, cyber
secure and offer efficiency gains and cost savings
• a collective resistance to change operating practices among institutions and the
labour force, which may arise in part from an ongoing social norm that places
greater trust in human control than machine control
c. Implications for policy and research
At a high level, the review’s findings imply that an integrated package of measures to
support innovation in freight handling and movement should include the following:
1. Continued investment in nationwide high-speed, high-capacity data transmission
networks (both fibre-optic and mobile).
2. Ensuring that legislative and regulatory frameworks are adapted to enable the
use of AVs on the public highway network. This includes giving due consideration
New Technology and Automation in Freight Transport and Handling Systems
5
to standards for vehicles, roadside infrastructure and the regulation of AV
operation on public highways.
3. Ensuring that the future rail freight strategy allows for the potential deployment of
‘rolling motorways’ on new sections of the rail network, along with complementary
transhipment points
2
, as is happening on the European continent.
4. Developing a strategic plan to support the private sector to adopt and develop
new systems of freight handling and movement, including:
(a) providing financial support for research and development programmes, with
trials objectively and fully evaluated (to generate compelling evidence of
efficacy relevant for knowledge transfer the sector); and
(b) training programmes to increase workforce capacity regarding the adoption
of new operating practices.
In addition, consideration might be given to calls (for example, by the Institute for
Public Policy Research (Lawrence et al., 2017) for the establishment of a ‘National
Robotics and Artificial Intelligence Ethics Authority’ to advise on the ethics of
automation. The authority’s potential remit could include:
• giving consideration to human safety in proximity to autonomous technologies
• examining liability issues in cases where autonomous technologies fail
• advising on socially equitable strategies to deal with circumstances where
human labour is replaced by automated technology
Finally, it is noted that further primary research in the form of an in-depth analysis of
the UK supply chain (considering different sectors, e.g. automotive, food, coal) is
required to identify the prospects for applying different automated freight systems.
Such an analysis could be carried out at regional and national levels, giving
consideration to the major freight corridors across the UK.
2
A transhipment point is a place located between origin and final destination where goods or
containers are in some way transferred from vehicle to vehicle, or vehicle to temporary storage
facility. Reasons for transhipment might be changing of the means of transport during the journey, or
consolidation/deconsolidation (e.g. combining small shipments into a large shipment or dividing the
large shipment at the end of a ‘trunk’ haul).
New Technology and Automation in Freight Transport and Handling Systems
6
Figure 1: Key technologies identified across the supply chain
New Technology and Automation in Freight Transport and Handling Systems
7
3. Introduction
An efficient freight transport system is essential to the economy and to ensure a high
quality of life. Intelligent transportation systems aim to increase the use of existing
transportation systems, capacity from the existing physical infrastructure, safety and
security, while at the same time decreasing the negative environmental impacts of
freight transport (Ranaiefar, 2012). Innovative solutions may support operators in the
organisation of freight management and handling activities at freight terminals and,
in particular, may promote intermodal
3
transport by reducing handling times and
costs at terminals (Gattuso and Pellicanò, 2014). Automated guided transport
systems and vehicles for commercial purposes were introduced in the early 1950s in
the USA and approximately 10 years later in Europe, driven by the mechanisation of
production, with the aim of optimising flows of materials and reducing labour needs.
Initial applications of automation were in production and warehousing contexts
(Flämig, 2016), but to date, automated freight transport systems are not used in
public open space, as they require a specific and dedicated infrastructure and
regulations.
Neuweiler and Riedel (2017) found that there is a gap in research related to
identifying competitive advantages, with autonomous driving entering the market. In
terms of ‘technology’, there has been great effort in investigating new technologies
for transport systems, and notable progress has occurred in recent years. However,
there has been limited investigation into the microeconomic and macroeconomic
benefits and costs of these developments, and more research is needed (Flämig,
2016).
The present report analyses, in an accessible way, the potential for new forms of
freight transport (i.e. automated freight transport systems) to replace or integrate with
current transport systems in the UK. It provides a state-of-the-art study with an
overview of past, current and future developments in automated freight transport
systems. Technology available now or in the short- and mid-term future is
considered. The review was commissioned by the Government Office for Science as
a contribution to the Foresight Future of Mobility project, which aims to explore
opportunities and implications regarding the transport system for the period up to
2040.
This report also provides a literature review and state-of-the-art summary of different
innovative systems for freight transport and logistics (see Section 5). It analyses
different applications to the supply chain and different transport modes, as well as
the advantages and disadvantages of the reviewed automated and innovative
systems. The literature review section ends with a focus on innovative solutions for
last-mile deliveries. Section 6 identifies and defines business models for different
automated systems, with a particular focus on road transport and platooning, which
3
Intermodal transport implies that more than one mode of transport (e.g., rail, ship, and truck) is used
to transport goods from origin to destination, avoiding any handling of the freight itself when changing
modes. The main reasons for inter-modality are to reduce direct cargo handling through the use of
standardised containers, improved security, reduced damage and loss, reduced overall delivery times
and reduced costs.
New Technology and Automation in Freight Transport and Handling Systems
8
emerged as an important topic due to upcoming technology development and the
ongoing process of defining regulations. Following this, an analysis of the
stakeholders involved and their needs is provided (Section 7). Section 8 analyses
the enablers of, and barriers to, the implementation of automated systems for freight.
Section 7 analyses the implications of introducing automated systems for freight in
relation to employment in the sector, for logistics service providers and for the
environment. Section 8 identifies specific relevance for the government in its role as
the potential promoter and fosterer of new technologies to improve the UK’s
competitiveness in the sector, reduce negative externalities
4
related to freight
transport and logistics, and support the UK economy’s growth and development.
Finally, the limitations of the study, some key issues that policymakers may wish to
consider, and identifying future research topics to address identified gaps, are all
considered.
4. Methodology
5
The review was conducted through two different and parallel approaches:
• consulting a pool of experts with a view to identifying relevant work published
worldwide on the review topics, including academic papers, reports, trials and
experiences, and any other evidence on the topic
• searching relevant documents through online web search engines, such as
Google and Google scholar
For each new technology, consideration was given to the state of technological
development and its impacts to date, and its applicability to different operating
contexts (defined in relation to modes of transport and location in the supply chain).
Candidate source documents were searched, selected and prioritised for inclusion
through a two-step filtering and ranking process, which first considered the relevance
and transferability of the evidence, then a further rating linked to the perceived
importance of the source.
The sources and their scores were recorded in a database (see Database in Annex).
Finally, the documents were identified for inclusion in the review, depending on their
rank scores against the defined criteria.
4
Externalities, or ‘externalised costs’ are those costs which arise from an economic activity which are
not met by either the producer of the goods or services or the consumer of them. Hence, they are
‘externalised’ onto the wider economy, society and environment. Classic examples are: air pollution,
with the costs being suffered by individuals in terms of poor health or picked up by public health
systems in addressing poor health; congestion, with the costs of delay increasing the transport cost
element in consumer prices; and climate change, where the main costs are likely to be met by future
generations.
5
A more detailed document is included in Appendix A.
New Technology and Automation in Freight Transport and Handling Systems
9
5. Literature review
This section focuses on the different applications of automation in the freight
transport and logistics sector. Different technologies, together with different topics,
are analysed, along with the benefits and limitations of innovative solutions.
The literature review begins by introducing automated loading systems at ports and
depots, and remote controlled units and stacking equipment. It then moves on to
road transport, introducing automated and potentially connected technology for
trucks (autonomous vehicles – AVs) and platooning
6
(connectivity combined with
limited/ partial automation), and identifies the benefits and limitations of their
application. The review analyses automated railway systems, and also focuses on
air transport and drones. It concludes with automated systems and new technologies
for last-mile deliveries in an urban environment (e.g. AVs, drones and 3D printing).
a. Automated loading systems at ports and depots
A container port represents a breakpoint in the supply chain (Franke, 2008). Being
an intermodal
7
transhipment point
8
, it is subject to differences in arrival and
departure time, with a lack of information that often causes lead time inefficiencies.
Automation in a container terminal can overcome issues due to spatial limits.
According to Tavasszy (2016), a container terminal’s efficiency can be improved by
automation: if the order of truck arrivals at a terminal is well known beforehand, yard
planning can be more efficient. For this reason, port terminals need to be
characterised by an efficient marine terminal part-ashore (Franke, 2008), and an
intermodal interface centre inland. In this ideal model of the Agile Port System,
the efficient marine terminal and intermodal interface centre are connected by a
dedicated railway line (Franke, 2008). The core idea of the Agile Port System is as
follows:
• handle as many containers as possible between vessels and trains, avoiding
storage in the terminal
• transport containers immediately between terminal and intermodal interface
centre by train
• sort containers between trains according to their final destination
• load and unload trucks which serve the nearby area at the intermodal interface
centre
6
For full definition see Footnote 1. ‘Platooning’ is the grouping of vehicles on a highway (usually with
data links) so that their control systems are temporarily linked. This connectivity offers limited
automation to the following vehicles in the platoon.
7
See Footnote 2 for a definition of ‘transhipment point’.
8
See Footnote 3 for a definition of ‘intermodality’.
New Technology and Automation in Freight Transport and Handling Systems
10
Figure 2: The Agile Port concept
The Agile Port concept (Figure 2) considers a combination of improved semi-
automated equipment that allows transhipment of containers between vessel and
train and vice versa directly at the quay, without a loss in performance. In fact, load
units may be stored close to the customer, instead of at the port terminal (Franke,
2008). The Port of Hamburg represents a good example, where Noell
9
improved the
original efficient marine terminal concept by elaborating the ‘Mega Hub’ concept,
through which 360 boxes could be transhipped between trains in 100 minutes.
A reduction in machinery and labour costs is the main benefit of the efficient marine
terminal, due to the redundancy of yard transfer vehicles. The system considers a
combination of improved semi-automated, ship-to-shore cranes; semi-automated,
cantilevered and rail-mounted gantry cranes; and a box mover based on rail-
mounted, automated shuttle cars driven by linear motor technology (Figure 3).
9
For further details please see the following patent listing:
https://patents.google.com/patent/US6778631B2/en
New Technology and Automation in Freight Transport and Handling Systems
11
Figure 3: Intermodal ship-to-rail transfer of containerised cargos, port in
Long Beach, California
10
Source: https://commons.wikimedia.org/wiki/File:Intermodal_ship-to-
rail_transfer.JPG
A number of studies have examined the automation of container-carrying vehicles
and cranes at ports. Moghadam (2006) conducted an economic study on the effect
of automation and semi-automation on loading, discharging and stacking processes
in terminals using Quayside Cranes, Straddle Carriers, Rubber Tyred Gantry cranes
and Rail Mounted Gantry cranes. The automated features examined were those on
‘post-Panamax’ cranes.
11
Moghadam found that if such devices were added to conventional quayside cranes,
container waiting times in the terminal were reduced; however, this should be offset
against the times where automated berths were unproductive (Moghadam, 2006).
Automation reduces the turnaround time for ships in port, and thus would produce
benefits for shipping companies. Moghadam suggests that the cost of investing in
automatic devices would be compensated for within months of operation; he further
notes the safety improvements arising from automatic devices, but considers them
hard to quantify monetarily. Automated rail mounted gantry systems were found to
be cheaper per container than semi-automatic, rubber-tyred gantry and straddle-
carrier systems.
Široký (2011) examines the benefits of automated guided vehicles (AGVs) and
automated stacking cranes (ASCs) at ports. The study consists mainly of
summaries of the automated devices’ technical characteristics, and does not identify
the systems’ drawbacks or weaknesses. However, the study is effective in
10
Yard hauler is parked adjacent to train; cargo is transferred to train by a rail-mounted gantry crane.
11
A post-Panamax crane is able to load and unload containers from a post-Panamax cargo ship.
Post-Panamax vessels are too wide to pass through the Panama Canal, meaning vessels about 18-
containers wide.
New Technology and Automation in Freight Transport and Handling Systems
12
highlighting some of these devices’ strengths and technical capacities. Široký states
that AGVs can convey freight between the quay and the stack yard.
AGVs have a number of strengths and advantages:
• all wheels rotate independently, allowing precision loading and unloading
• they are able to convey containers of varying lengths
• they work to schedule at high-speed, almost silently
• they can overtake each other
• they can refuel automatically (although they can also take on enough fuel to
work for several days without refuelling)
• they can move safely, due to laser detectors that register obstacles in their
paths
In addition some AGVs feature lifts, which can raise and lower loads. Lift AGVs can
decouple transport and storage processes, can further increase efficiency and
reduce the size of the fleet that is necessary.
ASCs also boast many strengths. They:
• can stack containers between one and five layers deep
• can move at speeds of up to 21kmh on tracks
• contain anti-collision precautions
• can save space
• can work in extreme conditions, including wind speeds of up to 10 on the
Beaufort Scale
• can position loads accurately
Together, AGVs and ASCs can provide automated solutions from quayside to stack
yard. The software running AGVs and ASCs can be integrated with other terminal
systems.
Information technology (IT) can be used to assess the effectiveness of automation.
Port container terminals vary according to some key aspects (type of water access,
maximum ship size, financial constraints, etc.). However, they need to be compared
to one another to evaluate efficiency and competitiveness (Wiśniki et al., 2017):
specifically, in order to understand what levels of automation are most cost-effective.
Thus, a tool which can take account of multiple criteria is useful.
Data envelope analysis (DEA) has been proposed as applicable for this purpose by
Wiśniki et al. (2017), who compared nine European port container terminals with
varying levels of automation. The DEA method allows us to identify which features of
New Technology and Automation in Freight Transport and Handling Systems
13
a port container terminal are key to its efficiency. However, one limitation of this
method is that the greater the number of characteristics of a terminal are considered,
the greater the number of ports are required for comparison. The analysis suggests
that increasing levels of technology in the less efficient terminals would increase
efficiency – by up to 219%, in the case of one port. However, Wiśniki et al. conclude
that efficiency is not necessarily dependent on high levels of automation in terminals.
DEA mathematical programming was able to compare and assess different
terminals, even though they differed from each other in multiple respects.
Mechanisation and automation are also improving the efficiency of transporting
freight by train in ports via marshalling yards, where trains are split up for different
destinations (Zářecký et al., 2008). In marshalling yards, individual wagons are
separated and often moved to new target tracks via a downward gradient. However,
manual work in a live marshalling environment can be dangerous, necessitating
constant awareness of multiple moving wagons. Here, automated systems can
reduce the need for a manual work element. By 2008, IT was being used to control
points in marshalling yards, control signals to drivers, relay information on the speed
of vehicles, assess the position of wagons and regulate the speed of wagons as
gravity takes them towards destination tracks. Such systems provide safety
functions, including helping to regulate the opening and closing of gates to the yards,
ensuring safe coupling to equipment, and controlling signals that indicate a driver
can continue. Thus Zářecký et al.’s (2008) main emphasis is on the safety
improvements available through automation.
The same can be said for automated docking. Distribution centres could become
more efficient if the order of truck arrivals is known beforehand. In general,
automated warehouses are successfully established worldwide, and there are
different automated tools and systems to support employees at a depot. Probably
the most common tools are voice-directed or light-directed picking tools, which have
not changed much over the years. Employees recognise that they can work faster
and more accurately and, according to Trebilcock (2011), logistics operators do not
aim to eliminate the human component; rather, they want to support employees to
reach their potential by eliminating walking, reading, waiting or any other extraneous
process in order to improve overall performance. Furthermore, companies decide to
have automated depots to create a safer and more ergonomic work environment,
especially if an ageing workforce is considered. In fact, European regulations are
looking increasingly at reducing the weight that workers can move at any one time,
or during a shift (this is also becoming a concern for some facilities in the USA).
However, labour is not the only reason for automation. Automated depots are more
flexible and can be ‘reprogrammed’ by considering new customers’ needs.
Automation is also justified by considering a holistic view of the supply chain, which
considers coordination with what happens in retail outlets in order to reduce
operating costs.
In the UK environment, probably the largest automated port is London Gateway
(Wainwright, 2015). Twice the size of the City of London, the Gateway hosts the
world’s largest cranes. Its development is ongoing, but following completion it will be
able to unload six cargos at once. DP World, a world leader in global trade
enablement, declared that the port will allow the reduction of 2,000 HGVs on the
roads every day, with a significant impact on the environment and economy.
New Technology and Automation in Freight Transport and Handling Systems
14
In particular, the Gateway reduced its carbon emissions by 28% per Twenty-foot
Equivalent Unit (TEU)
12
in 2016. The reduction was related to high automation to
handle the high number of containers (e.g. increased efficiencies and economies of
scale), the introduction of hybrid-electric shuttle carriers to the port’s operations, the
optimisation of energy usage, and reduced energy consumption in buildings (DP
World, 2017). Fully automated cranes on rails manage the stacks, but notably the
ship-to-shore quay cranes themselves are not automated, but manually operated.
Truck collection is also fully automated (i.e. vehicles arrive, are scanned and then
loaded to the bay), with a turnaround time under 30 minutes. Being highly
automated, London Gateway is not influenced by the weather in terms of operational
efficiency, and can operate when Felixstowe and Southampton have to close due to
poor conditions. This makes it more competitive than the other ports in the UK.
However, despite the strong advantages provided by automation, the port is not
performing as expected due to supply chain managers’ unwillingness to change their
operational behaviour.
13
This is limiting its ability to reach critical mass (e.g. 300,000
containers were handled during the first year of operation in 2015, rather than the
projected 3.5 million).
According to Moody (2016) the limited number of ports causes inefficient queuing of
container ships outside the port, which in turn creates inland freight congestion
issues.
14
In addition, there are many issues related to large container ships’ loading
and unloading operations, which take a long time. Furthermore, fixed scheduling
systems cause queues of lorries, which are loaded and checked slowly, which slows
down the process.
Another important area of development for maritime shipping is autonomous
shipping, with autonomous vessels that are equipped with detectors, sensors, high-
resolution cameras, advanced satellite communication systems, no crew members
on board, remotely human monitored and controlled from a shore operating centre.
Based on the autonomous shipping concept first introduced in the 1980s by Rolf
Schönknecht (1983), Rolls-Royce is collaborating with universities, designers and
manufacturers to explore the economic, social, legal, regulatory and technological
issues of autonomous shipping within the Advanced Autonomous Waterborne
Applications – AAWA project. Rolls-Royce (2016) expects remotely-operated local
vessels to be the first stage and in operation by 2020, with remote-controlled
unmanned coastal vessels by 2025, remote controlled unmanned ocean-going ships
by 2030, and autonomous unmanned ocean-going ships by 2035. While most of the
automation is introduced for container cargo, automated vessels might be connected
to different investments in automated terminals for some of the bulk traffic.
12
The twenty-foot equivalent unit (TEU) is an international recognised unit of cargo capacity, usually
used to describe the capacity of container ships and container terminals. It is based on the volume of
an intermodal container that is 20 feet in length (6.1 m). It is a standard-sized metal box that can be
easily transferred between different modes of transportation, such as ships, trains and trucks.
13
If supply-chain managers are happy with their operational and strategic plan, they are unwilling to
change it as this might mean incurring longer times and higher costs, with a strong impact on supply-
chain competitiveness.
14
Container ships must wait to be allowed in port for loading and unloading operations. This
generates correlated inland congestion, due to the time that trucks need to wait for their containers to
be delivered and loaded to or unloaded from the vessels.
New Technology and Automation in Freight Transport and Handling Systems
15
However, despite the high benefits in terms of crew cost reduction, there are major
concerns about safety in navigation and liability for autonomous vessels sailing in the
open sea, due to weather conditions.
b. Self-driving or remote-controlled units and stacking
equipment
Self-driving units can be used on private premises. For example, automated vehicles
can be used at a container terminal to move containers between the quay cranes
and stacking area, with the aim of shortening routes, reducing empty trips and
achieving optimal utilisation of all resources (Flämig, 2016). They also can be used
in cargo hubs, where several trucks load or unload their cargo. The services are
usually planned per single truck, while the terminal’s facilities and resources (crew,
cranes and space) are shared to a large degree by the clients, their trucks and
cargo. Truck arrival times at a loading and unloading dock are usually uncertain: they
can vary between seconds and hours, depending on unexpected issues along the
supply chain that generate delays. For this reason, predicting the exact order of truck
arrivals at a terminal is difficult or impossible, making it challenging to plan the stacks
on the container yard because ideally they should be aligned for the order of
servicing.
For example, if truck A comes before truck B, container A should be on top of B, not
the other way around. Truck platooning may reduce the yard planning problem
significantly at terminals, because the order of trucks is fixed and known with
platooning. This implies that the number of repositioning moves with containers due
to modal shift
15
at a container terminal can be reduced. Estimating the benefit then
relies on assumptions of how many moves will be saved, and at what cost per move
(Tavasszy, 2016).
If we consider the case of a terminal with 35–50 million TEU a year, of which 40–
80% are moved by road transport, then automation could result in two to three fewer
repositioning moves of containers within the terminal ‘stacks’ on average per
container. As each container repositioning move has an individual cost of about €30-
40, then the total savings at the terminal per year would be very significant
16
:
• in the optimistic case that all trucks arrive at terminals in platoons, so the
stacking of containers is fully optimised, the cost reduction would be between
€1 and €4 billion a year
• in an initially more likely scenario that just 10–20% of trucks arrive in platoons,
the savings amount to €100 to €800 million a year. (Tavasszy, 2016)
15
Modal shift means transfer a load unit from one mode to another one with the aim of reducing costs
for the operators, or to achieve policy objectives e.g. reduce road congestion. Indeed, it usually refers
to the shift from road transport to another mode, as the former is usually the most congested mode.
16
According to Tavasszy (2016), given the uncertainties in these variables it is easiest to work with
plausible ranges of numbers.
New Technology and Automation in Freight Transport and Handling Systems
16
c. Freight transport of the future
This section reviews automated systems for road transport and last-mile deliveries.
AVs, platooning of vehicles for the long haul, as well as new solutions for last-mile
deliveries are considered.
Autonomous vehicles for road transport
Probably the first example of the use of automated trucks was at one of the world’s
largest iron-ore mines in Australia in the 1990s, which aimed to overcome difficulties
in staffing due to dangerous shift work in the outback and demanding logistical
requirements in terms of personnel planning and staff transfer (Flämig, 2016).
Numerous pilots on automated and electric transport systems have been carried out
since the mid-1990s: e.g. the European projects Chauffeur I and II, Safe Road Trains
for the Environment (SARTRE), Cooperative Mobility Solution for Supervised
Platooning (COMPANION), the Californian PATH Program, the German KONVOI-
Projekt and the Japanese ITS Project by the New Energy and Industrial Technology
Development Organization, NEDO. These projects involved multiple trucks or a
convoy of lead trucks and following passenger cars that were driven at up to 90km/h,
with a minimum gap of 4m (Flämig, 2016).
Compared with other modes of transport such as aviation and rail, automation for
road transport has been less adopted to date, primarily due to the development level
of the technology for road environments, which are much more complex than the
segregated rail and air systems involved. As automation technology continues to
develop it will be important to ensure that the complex regulatory and legal regime
keeps up with the evolving questions and challenges this technology poses for
users, goods and society.
Nonetheless, AV technology potentially has significant impacts in terms of safety
improvements, fuel consumption reduction and consequently cost reduction, as well
as increased efficiency and flexibility: for example, due to the reduced effect of
driver absence from work. According to Guerra (2015), the acceptance and adoption
of AV trucks is likely to be higher than AV cars, despite high commercial prices.
According to Neuweiler and Riedel (2017) (Table 1), there is a lack of research on
autonomous trucks: only few published articles examine barriers and advantages.
They identified opportunities for the logistics market, which have been recognised by
several authors.
New Technology and Automation in Freight Transport and Handling Systems
17
Table 1: Summary of main opportunities identified in the review by Neuweiler
and Riedel (2017)
Opportunity
Sources
Increased safety
Roland Berger (2015, 2016); Bagloee
et al. (2016); DHL (b) (2014);
McKinsey Global Institute (2013)
Decreased transportation costs
Roland Berger (2015, 2016), Hars
(2015); Viereckl et al. (2015)
Decreased fuel consumption
Roland Berger (2016); Viereckl et al.
(2015); McKinsey Global Institute
(2013)
Environment and emission
Bagloee et al. (2016); DHL (b) (2014)
Improved truck utilisation
Roland Berger (2016); Viereckl et al.
(2015); DHL (b) (2014)
Better road utilisation
Roland Berger (2016); Bagloee et al.
(2016); Viereckl et al. (2015).
Better driver utilisation
Roland Berger (2016); McKinsey
Global Institute (2013)
Source: Neuweiler and Riedel (2017), Table 1
Combining connectivity with automation (be that limited or full), platooning is one of
the most promising functions of automated vehicle technology for freight. According
to COMPANION (2016): “platooning means grouping vehicles into platoons to gain
advantages compared to individually driving trucks”. In this way, the distance
between vehicles decreases by allowing them to accelerate or brake simultaneously.
According to SARTRE (Chan, 2014), platooned vehicles are connected through
smart technology that allows them to travel together with automated control. This
results in safety, efficiency, congestion and emission-level improvements. Such
benefits are facilitated by vehicle-to-vehicle (V2V) communication, which allows the
formation and maintenance of a close-headway formation (i.e. reduced distance)
between vehicles, by keeping them under coordinated control both longitudinally and
laterally (Besselink et al., 2016). ‘Longitudinal’ control refers to the distance between
the vehicles, whilst ‘lateral’ control refers to the position of a vehicle within a lane on
a highway
17
.
However, as Tavasszy (2016) notes, truck platooning technology has not been fully
defined yet – it is still at the demonstration and pilot stage. Different aspects related
to technology, logistics, regulatory and business need greater definition. Moreover,
advances in research and development from the technology side have not been
matched by investigations into, and quantification of, the potential benefits for road
17
The lateral position should normally be fairly central to minimise the risks of collisions with vehicles
in other lanes, but very precise and standardised positioning within a lane would result in fast wearing
of a particular alignment, so it is likely that platooning systems will vary the running position to spread
this wearing effect across a wider band of the lane.
New Technology and Automation in Freight Transport and Handling Systems
18
users, logistics companies and the environment. Similarly the potential impact of
truck platooning on both rail and sea freight is another unknown, which may change
its desirability.
Figure 4 shows a taxonomy and definitions for automated driving provided by the
Society of Automotive Engineers International’s J3016 report
18
in January 2014. The
report’s six levels of driving automation span from none (Level 0) to full (Level 5).
Traffic congestion reduction, fuel consumption improvement and increased capacity
by vehicle throughput are the benefits identified from platooning. Early-generation
platooning technology requires the driver to be responsible for steering, implying
Level 1 automation. Platooning testing is proceeding for Level 2 automation, where
both longitudinal and lateral control are managed by the automated system.
As mentioned previously, in Europe, truck-platooning efforts began in the 1990s with
Chauffeur, followed by Chauffeur II, and in the 2000s by the SARTRE project
(Shladover, 2012). In 2016, an experiment was carried out featuring six convoys of
truck platoons belonging to different European trucking brands, originating from
various factories in Sweden and Germany and arriving in Rotterdam. During the
2000s, Japan also began to support truck platooning studies under the Energy ITS
program. In the same period, similar research was supported by the US Department
of Transportation and US Army to confirm technical feasibility and fuel economy
benefits (ATA Technology and Maintenance Council, 2015).
The Ministry of Transport of Singapore and PSA Corporation signed agreements
with Scania and Toyota to design, develop and test-bed an autonomous truck
platooning system for Singapore’s port. It is a two-step truck platooning trial
developed from January 2017 to December 2019, in which trucks transport
containers from one port terminal to another (Ministry of Transport of Singapore,
2017).
Most major truck manufacturers have already begun, and will continue, platooning
tests in cooperation with government agencies all over the world, claiming that trucks
equipped with platooning technology will come on the market by 2020. Trucks
equipped with radar and V2V systems may form, join or leave a platoon on the
highway. The systems do not require changes in signage and lane markings, but do
require changes to spacing requirements. However, even though the technology will
permit ‘cutting in’ by other vehicles, long lines of platooning trucks may create some
difficulties for the operation of other vehicles in traffic. For the USA, states that have
‘following too closely’ statutes in force should review and amend these, as they might
create an impediment to platooning operations. Other regulations necessary to ease
the operations in traffic, such as designated lanes, might also be considered.
Although the current UK truck platooning trials can occur without legislative change.
18
The document is titled: “Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle
Automated Driving Systems J3016_201401”. It describes the full range of levels of driving automation
and related terms and definitions.
New Technology and Automation in Freight Transport and Handling Systems
19
Level
Name
Narrative Definition
The Dynamic Driving Task
Fallback
performance of
Dynamic
Driving Task
(Operational
Design
Domain)
Sustained lateral
and longitudinal
vehicle motion
control19
Object and
Event
Detection and
Response
Driver performs part or all of the DDT
0
No Driving
Automation
The performance by the driver of the
entire DDT, even when enhanced by
active safety systems.
Driver
Driver
Driver
n/a
1
Driver
Assistance
The
sustained
and
ODD
-specific
execution by a
driving automation
system
of either the
lateral
or the
longitudinal vehicle motion control
subtask of the DDT (but not both
simultaneously) with the expectation
that the
driver
performs the
remainder of the
DDT
.
Driver and
System
Driver
Driver
Limited
2
Partial
Driving
automation
The
sustained
and
ODD
-specific
execution by a
driving automation
system
of both the
lateral
and
longitudinal vehicle motion control
subtasks of the
DDT
with the
expectation that the
driver
completes
the
OEDR
subtask and
supervises
the
driving automation system
.
System
Driver
Driver
Limited
ADS(“System”) performs the entire DDT (while engaged)
System
System
Fallback-
ready
user(becomes
the driver
during
feedback)
Limited
3
Conditional
Driving
Automation
The sustained and ODD-specific
performance by an ADS of the entire
DDT with the expectation that the
DDT fallback-ready user is receptive
to ADS-issued requests to intervene,
as well as to DDT performance-
relevant system failures in other
vehicle systems, and will respond
appropriately.
4
High
Driving
Automation
The sustained and ODD-specific
performance by an ADS of the entire
DDT and DDT fallback without any
expectation that a user will respond to
a request to intervene.
System
System
System
Limited
5
Full Driving
Automation
The sustained and unconditional (i.e.,
not ODD-specific) performance by an
ADS of the entire DDT and DDT
fallback without any expectation that a
user will respond to a request to
intervene.
System
System
System
Unlimited
Figure 4: Levels of Automation
Source: Society of Automotive Engineers International (SAE). Reproduced
Taxonomy and Definitions for Terms Related to Driving Automation Systems
for On-Road Motor Vehicles J3016_201806 (2018).
19
In earlier SAE versions, this table was sometimes simplified: Sustained lateral and longitudinal
vehicle motion control was simplified to steering, accelerating and braking. Object and Event
Detection and Response was simplified to Monitoring of driver environment. Operational Design
Domain referred to which driving modes the system works for.
New Technology and Automation in Freight Transport and Handling Systems
20
The benefits achievable through AV trucks are as follows.
Safety improvement. AVs are designed to drive safely, respecting regulations and
laws, thus reducing the number of risks and crashes. The US National Highway
Traffic Safety Administration (2016) estimates that V2V and vehicle-to-infrastructure
(V2I) technologies could avoid or mitigate the severity of crash outcomes up to 80%,
looking at 20 years after their implementation. In 2014 there were 3,903 fatalities in
the USA resulting from large truck-involved crashes (Short and Murray, 2016). Of
those fatalities, 17% were the occupants of large trucks, 73% were the occupants of
other vehicles and 10% were non-occupants. As noted by the Department for
Transport (2015), 94% of the road deaths and injuries in the UK are due to human
error, and the portion of these involving HGVs could be avoided if fully-automated
trucks are used. The reduction in collisions implies lower overall insurance claims
and premiums – a benefit potentially rising to 90% lower than currently, when
automation is widespread (Celent, 2012). However, the level of information provided
during the trials by track testing may not provide enough evidence for an insurance
company to provide cover. In this case, self-insurance either by the vehicle operator
or another trial stakeholder would be required (Ricardo, 2014).
Improved drivers’ working time. Drivers may be able to execute other tasks beside
their driving task (Tavasszy, 2016), such as processing documentation or assisting
customers. In general, it can be said that automated systems can increase the
productivity of single truck–driver combinations (Tavasszy, 2016). The European
directive allows a consecutive driving time of 4.5 hours, which can be repeated after
a short break. This driving time constraint limits the action radius of a single truck–
driver combination to about 720km, travelling at an average speed of 80km/h. If a
delivery needs to be made within one day in Europe beyond this distance, at least
two drivers need to be on board the truck (Figure 4). The study carried out by
Tavasszy (2016) considers that “the second truck’s driver’s time is not counted fully,
together they can increase their range of travel. If the work time of the second driver
would only count for 50% and the two would change leading positions after 3 hours,
they could increase their daily travel range each to 960km” (Figure 5).
New Technology and Automation in Freight Transport and Handling Systems
21
Figure 4: Time–distance graph for single driver trucks with current European
driving time directive.
Source: Tavasszy (2016), Figure 1; reproduced with permission.
Figure 5: Time–distance graph for adapted driving time directive and
platooning
Source: Tavasszy (2016), Figure 2, reproduced with permission
New Technology and Automation in Freight Transport and Handling Systems
22
In terms of economic benefits, Tavasszy (2016) estimated that on average, €8.8
billion a year could be saved through truck platooning. Moreover, driver salaries are
a large share of direct costs. If automated trucks are used, drivers can perform other
tasks during the journey, so the operating costs associated with them can be
reduced. Wadud (2017) reports that this reduction in salary costs can be 60%, with
the residual cost being for loading and unloading operations at the origin and
destination.
Energy and fuel consumption reduction. AV technology allows more efficient
driving and reduction in fuel consumption, which has been well documented.
Especially with platooning, a reduction in travel time and fuel consumption is
achievable by enabling higher effective speeds and lowering air resistance (Scribner,
2016), which benefits second and further following trucks. The potential overall
savings in fuel costs for Europe have been estimated at between €400 million and €6
billion a year, with an average value of €1.9 billion a year (Tavasszy, 2016).
However, the actual rate of fuel savings is still uncertain, and depends on the
interaction of a platoon with other road users (Bakermans, 2016).
Tsugawa et al. (2016) simulated an automated truck platooning system of three
trucks with a constant speed of 80km/h. The trucks were unloaded. The
measurements indicated that with a 10m gap there was a 13% energy saving, and
18% when the gap was 4.7m.
The experiment was repeated with the trucks loaded. In this case, the fuel saving
was 8% for the 10m gap, and 15% for the 4.7m gap (Table 2).
Table 2: Reduction of fuel consumption based on theory, simulation and test
for 14-ton and 28-ton trucks
Fuel
consumption
Theoretical
Simulation by
Daimler
Measurement by
Daimler
Simulation
with PELOPS
by KONVOI20
First vehicle
2.17% (14T)
1.64% (28T)
2% (28T)
6% (14T)
2% (14T)
Second vehicle
38.1% (14T)
28.8% (28T)
19% (28T)
21% (28T)
11% (28T)
Source: Tsugawa et al. (2016), Table III
Figure 6 shows a computational fluid dynamics simulation of two trucks, which
indicates that the air pressure is low with a short gap in the platoon, and increases
as the gap between the vehicles increases. The reduced air drag results in lowered
implied fuel consumption, which has been quantified as at least 5%.
20
Results are based on a simulation study that was performed in the KONVOI (Development and
Examination of the application of electronically coupled truck convoys on highways) project using the
software PELOPS (Program for the development of longitudinal traffic processes in system relevant
environment), which takes into account the three relevant elements of traffic— route/environment, driver
and vehicle—and their interaction. (Please refer to Tsugawa et al. (2016) for more details).
New Technology and Automation in Freight Transport and Handling Systems
23
Figure 6: Simulation of air pressure for two trucks for different inter-vehicle
distances
Source: COMPANION (2016), Figure 6
Reduction of congestion and increased road capacity. The freight industry faces
significant but potentially avoidable additional costs every year due to congestion
(Torrey IV, 2016). Estimates for all UK road users recently placed the lost time cost
at nearly £40 billion for 2017, with the UK performing as the 10th most congested
country out of 38 surveyed (Inrix, 2018).
As well as the monetised cost of wasted time, fuel consumption is higher in unstable
traffic flows compared with free-flow driving conditions. AV technology will allow
fleets of AV trucks to travel more efficiently under different traffic conditions. V2V and
V2I technology will enable communication between trucks and other road system
components (i.e. vehicles and infrastructure), allowing mitigation of ‘stop–start’,
increasing travel speed and improving overall travel conditions, so creating benefits
beyond the freight sector.
Reduced congestion due to AV trucks is likely to be a practical application first on the
motorways, as automation is more likely to be introduced in the short term within
such a relatively controlled environment (Ginsburg and Uygur, 2017). In a study
carried out in the USA, more than 75% of HGV drivers in Illinois reported problems
finding safe parking, and 58% contravene parking regulations three to four times per
week (Oberhart and Perry, 2017). This problem can be mitigated after the
introduction of connected vehicles and AVs for freight, even though further study is
still needed to understand and quantify the effect, as no amount of technology can
reveal nearby parking opportunities if none are actually available. However, the
parking requirement may go down due to the application of more efficient
technologies, just-in-time arrival could be more precise with managed traffic flows,
and there may be reduced needs for driver layover due to maximum driving hours
regulations.
In addition, identifying locations for warehousing can be influenced by connected
vehicle and AV applications. This could change truck volumes on roads, but as in the
case of parking, the importance of this effect is difficult to estimate. For this reason,
private party stakeholders of freight (e.g. logistics companies, third-party logistics
and large shippers) should be involved in the planning process (Ginsburg and Uygur,
2017).
New Technology and Automation in Freight Transport and Handling Systems
24
Despite the high potential benefits from autonomous trucks and platooning, their
applicability to the UK context might be not possible or convenient. As suggested
above, UK motorways are among the most congested in Europe, and this technology
might not work very well if private cars continually require platoons to break and
reform. Therefore, while the benefits for the UK have been theoretically identified
(e.g. reduced fuel consumption, improved logistics schedule due to reduced time
delays, improved safety due to accident reduction, likely CO2 reduction, reduced
congestion
21
– all these benefits generate an overall cost reduction) (Ricardo et al.,
2014), road trials are needed to test the responsiveness of the system when
integrated within the whole transport system.
To this end, the Department for Transport and Highways England have
commissioned the first real-world operational trial of platooning on UK roads (TRL,
2017), planned for 2018. The real-world trial will identify if potential benefits can be
realised in practice, and therefore if the UK environment is suitable for this
technology.
Successful implementation of platooning on UK motorways will be more possible if
the road infrastructure is adapted to host automated transport systems. This
challenge might be variously addressed by equipping motorways and roads with
specific sensors (instrumented highway), or defining dedicated lanes for AVs and
platooning, in order to limit the interaction with other types of traffic.
Automation for railway systems
The European Union (EU) has sought to support such innovations via multiple
research and development projects. The following enhancements to the EU rail
freight system were identified as priorities by the Capacity4Rail (2018) project
(Nedall 2017; Ricci 2017):
• deployment of wagons that can tolerate axle loads of up to 30 tonnes, with
higher and wider gauges (to handle larger containers), lower floor heights (to
facilitate container transfer), electro-pneumatic brakes and automatic couplers
• deployment of (driverless) electric locomotives that are capable of longer
configurations (up to 2,000m), and heavier trains – e.g. providing 400kNs of
traction effort and capable of handling 25-tonne axle loads
• wider deployment of transhipment terminals that enable the efficient (roll-on,
roll-off) transfer of containers between road and rail
• completion of the European Rail Traffic Management System (ERTMS) digital
signalling system
21
Reduced congestion might be achieved if existing infrastructures are used efficiently. Platooning
can ensure better use of road space, related to the reduced gap between vehicles.
New Technology and Automation in Freight Transport and Handling Systems
25
Currently, two new projects are in progress under the Horizon 2020 programme,
although neither project has reported findings to date. The Automated Rail Cargo
Consortium (European Commission 2017a) project is examining three areas:
1. the use of automated freight trains
2. the application of automated processes at transhipment nodes
3. timetable planning.
The Smart Automation of Rail Transport (SMART) project is researching technology
to deliver obstacle detection and automatic, ‘forward-looking’ braking systems that
offer short-distance wagon recognition for shunting onto buffers (using thermal
cameras, image intensifiers and laser scanners). The SMART project is also aiming
to develop a real-time marshalling yard management system (European Commission
2017b).
Wiegmens et al. (2007) examined barriers to the adoption of new technologies in the
context of rail freight transhipment terminals. They identified that costs of new
technology are often prohibitive; that efficiency gains are often not perceived to be
sufficient to justify investment; and that efficiency gains may not be accrued to
terminal operators, hence reducing their incentive to invest. Moreover, the
transhipment market was perceived to be relatively ‘stable’, and this is argued to be
a barrier to innovation, for example through technology ‘lock-in’ (institutional
resistance to change), at least relative to unstable market contexts, where
innovations tend to be adopted more quickly.
The Department for Transport (2016a) published a strategy for rail freight in 2016,
which highlighted three technical innovations that have been taken forward in the UK
market through the Freight Technologies Group: Network Rail and the freight
operators:
1. Timetable Advisory System (SNC-Lavalin, 2017) – this equips train drivers with
software, hosted on tablets, to track train progress against the timetable. The aim
of the system is to improve driving efficiency. Simulations predicted energy
savings of between 5% (Rail Safety and Standards Board, 2010) and 8%
(Davies, 2014).
2. Freight Collaborative Decision Making system – this offers real-time information
boards at freight terminals, indicating the arrival times of freight services, to
improve decision-making and operational efficiency.
3. Mobile Consisting Application – before a loaded freight train departs, it is
necessary to log the ‘consist’ of the train (i.e. the nature of the carriages forming
the train) with Network Rail. This previously involved manually faxing a written
note to Network Rail for input by a data entry clerk. The Mobile Consisting
Application enables consist data to be transmitted directly from freight terminals
via software loaded onto a tablet device.
Alongside these innovations, digital signalling (as offered by the ERTMS) is being
strategically deployed at pinch points on the network (currently available on the
Thameslink and Crossrail routes) to unlock rail freight capacity, and hence increase
New Technology and Automation in Freight Transport and Handling Systems
26
the competitiveness of rail freight operations relative to road haulage. The ERTMS is
being developed to harmonise train control systems across EU borders. This
simplifies the signalling equipment requirements for cross-border rolling stock,
leading to cost savings, and offers up to 40% more network capacity (through the
deployment of continuous communication systems, which enable reduced running
headways) (European Rail Traffic Management System, 2014).
The Rail Freight Strategy (Department for Transport, 2016a) was accompanied by
‘Future potential for modal shift in the rail freight market’, a review conducted by Arup
(2016). This identified opportunities for innovation in locomotive technology, noting
that network electrification offers fuel and emissions savings. The alternative,
shorter-term option is to establish ‘handover’ connections to electrified networks,
where wagons can be switched to diesel haulage for the last mile. Further research
is recommended by Arup (2016) into what are considered to be ‘disruptive future
technologies’, including alternative fuel sources (liquid nitrogen gas or hydrogen) and
driverless locomotives.
There is additional potential to utilise the new (and hence modern) specification UK
rail lines (HS1 and 2) as ‘rolling motorways’, whereby HGV containers are
transported via rail for the longest section of the journey – for example, maximising
network utilisation at night. A number of (part-automated) systems have been
deployed already across the European continent to facilitate the efficient transfer of
containers between HGVs and train wagons (Arup, 2016). These include:
• CargoBeamer (2017) – a modular transhipment terminal system. Containers
are transferred from trucks onto CargoBeamer pallets, which are then
automatically (horizontally) loaded and unloaded onto CargoBeamer wagon
trains (via ‘side arms’ which slide under and engage with the pallets). Up to 36
semi-trailers can be loaded in 15 minutes. Modules are pre-cast in concrete,
enabling quick and easy installation of new transhipment terminals.
• The Lohr Railway System (2017) – currently deployed between Chambéry and
Turin, and Luxembourg and Perpignan, which is being rolled out more widely,
including at the Channel Tunnel rail link. Low floor wagons ‘swivel’ at
transhipment points, enabling containers to be driven on to and engaged with
the wagon (via a roll-on, roll-off configuration). Vertical loading and unloading of
containers via cranes is also possible.
• The InnovaTrain (2017) suite of systems – which is deployed in parts of
Switzerland. This consists of a ‘ContainerMover’ system mounted to the truck
chassis, enabling (driver-operated) lifting and horizontal transfer of containers
between trucks and train wagons. A second ContainerStation system enables
containers to be removed from wagons and stored at yards, yielding truck
loading and unloading times of under 2 minutes.
Automation for air transport
Since the early 20th century, stabilisation systems and autopilot functions have been
adopted for aircraft. Drones appeared for the first time in military, police and
firefighting applications. Unmanned aerial systems and vehicles are also used in civil
applications, for example to inspect remote areas after storms or fires, in film
productions and industrial inspections. Recently, some pilot studies have been
New Technology and Automation in Freight Transport and Handling Systems
27
carried out to explore the potential application of drones to transport goods in urban
areas (for more information about drone applications for last-mile deliveries, see
Section 3.3.4) (Flämig, 2016). Another potential application of drones is the transport
of lightweight, high-value goods point-to-point, such as blood samples being
transported between hospitals, or in remote regions. In Rwanda drones are already
being used to deliver life-saving medicine to rural areas (McVeigh, 2018).
Drones also can be used for handling tasks in a warehouse. This is happening in the
USA, where they replace humans on foot or operate fork-lift trucks and mechanical
lifts. Drones were found to be operationally cheaper and more accurate, because
they help to reduce the number of human errors with inventory (e.g. misplaced items
and faulty inventory records in the warehouse). Argon Consulting argues that two
drones can carry out the work of 100 humans (i.e. in terms of handling, picking and
order preparation) over the same time period with an accuracy close to 100%, with
warehousing and logistics cost savings (Jackson, 2017). A more advanced
technology has been launched by the French firm, Hardis Group, which uses an
inventory-scanning drone (i.e. EyeSee). EyesSee is fully automated and easy to use,
with no installation, no infrastructure adaptation and no driver.
These developments are both recent. To date, independent analyses of efficacy
were not found in the literature, but the potential for cost reductions and competitive
advantage for those who adopt them seems high. The investment capital
requirements are also high and can be uncertain: media reports in February 2018
indicated that the online grocery retailer Ocado recently had to obtain an additional
£150 million in shareholder investment to enhance functioning of its robotised
warehousing (Wood, 2018).
However, the significant potential impact on the currently growing employment sector
of warehousing should be noted. Political strife may not be a barrier to adoption in
newly-opened warehouses, as no humans will be obviously replaced. However, the
cumulative effects of automation across the sector, and indeed the economy, may
grow to become a significant topic of political debate, and potentially a barrier to
adoption.
Innovative solutions for last-mile deliveries
As well as being fundamental to the economy, the transport sector imposes
significant costs on society in the form of traffic congestion, road collisions and
health and environmental impacts (Korzhenevych et al., 2014). These impacts are
more concentrated in urban areas, due to high density of activity. They are also more
significant due to the high density of people, resulting in high exposure. Freight
transportation, mostly using diesel vehicles of medium and large dimensions, plays
an important role in the ‘urban transport problem’.
Different solutions have been developed in recent years to reduce negative
externalities related to these increasing freight flows in urban areas. In terms of
emerging solutions, there has been enthusiasm among commentators that shared-
resource economic models can both create new commercial opportunities, and
address transport policy problems. Korzhenevych et al. (2014) identified that
stakeholder collaboration through sharing can be an important tool for making the
urban freight transport system more efficient and effective. Collaborative strategies
New Technology and Automation in Freight Transport and Handling Systems
28
are commonly used in the field of supply-chain management (Montoya-Torres et al.,
2016). Collaboration requires information sharing, and therefore confidence and
trust, between the actors involved in the process.
However, in terms of new technologies and automated systems, the literature offers
a smaller volume of material on last-mile deliveries and urban goods distribution
rather than port-related initiatives, rail transport and long-distance haulage. The main
findings of this evidence review are presented on the next sections.
AVs. Emerging solutions for last-mile deliveries draw on the innovative technologies
that have mostly been introduced above. As in the case of longer supply-chain links,
AVs are proposed to make last-mile deliveries. AVs for the last mile offer more
routing flexibility in a complex network environment (Priemus et al., 2005). According
to Alessandrini et al. (2015), the potential applications of AVs for urban freight
movement relate to:
• just-in-time restocking of shops from remote warehouses;
• drop-off points for last-mile deliveries at houses or small offices; and
• waste collection and transportation.
A noteworthy application of AV systems for urban freight transport is represented by
a ‘light duty automated transport vehicle’: a physically connected vehicle train
consisting of a single automated ‘driver’ module (in fact, driverless), and a number of
standardised, interchangeable cargo modules which can be adapted and shaped
depending on the specific tasks and requirements. Figure 7 represents the concept
of a multi-trailer freight train.
Figure 7: Representation of an automated multi-trailer vehicle for last-mile
deliveries
Source: Alessandrini et al. (2015), Figure 6
An example of the modular approach is the Cargo Hopper System in Utrecht,
powered by a solar and battery-electric motor and comprising three containers which
New Technology and Automation in Freight Transport and Handling Systems
29
can be loaded on or off the undercarriages by a forklift. However, this system is
human-driven, not automated.
Alessandrini et al. (2015) suggest that this type of solution is likely to be applicable to
support last-mile deliveries performed through an urban consolidation centre. In this
case, goods are consolidated by destination and collected in modules, which are
accessible by customers who want to take their shipments through secured access:
e.g. credit card, smartcard or near-field communication (NFC) devices. This
integrated solution can result in reduced emissions from last-mile deliveries, due to
both the nature of the electric automated vehicles and the efficiencies brought by
urban consolidation centres. Operating cost reductions (e.g. driver labour cost
avoided),
22
safety improvements and reduced loading bay needs are achievable with
this system.
Other emerging solutions are considered within the European Freight Urban Robotic
vehicle (FURBOT) project. Cepolina (2014) proposes a mobile packstation which,
unlike the Amazon locker system
23
, does not have a fixed position as a solution for
urban deliveries. DHL Parcel Germany runs the Packstation service, which allows
customers to self-collect parcels, 24 hours a day, seven days a week. FURBOT
integrates the Packstation into the Urban Consolidation Centre concept. The goods
are unloaded in the location where they are required through the mobile packstation
in a timely manner (Figure 8).
Figure 8: FURBOT and the mobile Packstation concept
24
Source: Cepolina (2014), Figure 4
22
Every year, the Transportation Institute at Texas A&M University produces the Urban Mobility
Report, which shows statistics for the annual costs of congestion in the USA. The report considers
three main cost components for congestion delays: (1) the value of time for personal travel (estimated
at $16 per hour); (2) the value of additional driver time and other operating costs for large trucks
(estimated at $88 per hour); and (3) the cost of excess fuel consumption (based on prevailing prices
for petrol and diesel). Source: Anderson et al. (2014).
23
Amazon Lockers are self-service kiosks where customers can collect or return their Amazon
parcels at a time convenient for them.
24
LBL – Less than full Box Load, UDC – Urban Distribution Centre (also called Urban Consolidation
Centre)
New Technology and Automation in Freight Transport and Handling Systems
30
The vehicle proposed by the FURBOT project (Figure 10) integrates a mobile robot,
a van and a forklift. Driving assistance is provided to the driver for emergency
braking, obstacle avoidance, parking assistance, itinerary assistance and adaptive
speed control (FURBOT, 2014). It is claimed that these characteristics would enable
FURBOT to operate in city-centre locations which have access restrictions for
traditional vehicles (Molfino et al., 2014). The goods lock-and-release on
identification system also could resolve temporal conflicts between freight and
passenger traffic in city centres (Flämig, 2016), as the Packstation is delivered in off-
peak hours.
Loading and unloading operations would be automatically performed through a
robotised procedure (i.e. robotised forklift), which benefits from a customised design
for both vehicle and loading units (i.e. boxes). This makes the driver’s tasks easier
(FURBOT, 2014). The vehicle is equipped with sensors providing information about
the internal state of the vehicle and the environment outside. Conflicts with other
road users are avoided, and congestion is reduced, thanks to the use of specific
loading and unloading bays.
Figure 9: The FURBOT vehicle
Source: FURBOT
According to Cepolina (2014), FURBOT can be used as a single transport unit, but
also more effectively as a multi-agent system (i.e. fleet of vehicles).
In the UK, self-driving vehicles for passengers and goods have been tested in recent
years. In terms of last-mile delivery, the Gateway project (Greenwich Automated
Transport Environment) is notable. Led by TRL and funded by the UK Government
and industry, the project focuses on demonstrations in Greenwich, which include the
use of a zero-emission CargoPod with a carrying capacity of 128kg for last-mile
deliveries in a residential area.
The CargoPod load is characterised by eight slots. When it arrives at a specific
customer’s stop, the corresponding slot lights up, and the customer can open the
door to collect their goods, such as groceries, by pressing a button. The project
involves Ocado, the online grocery retailer. Results show that the system could
efficiently replace traditional delivery vehicles. Their wider adoption will depend on
both the technical ability to build and operate self-driving system, and regulations
ensuring their use is safe and legal, Some customers choose home delivery because
they have limited lifting ability: normally, the goods are brought into the home by the
delivery agent. An issue which the cargo pod does not resolve.
New Technology and Automation in Freight Transport and Handling Systems
31
Another option for last-mile deliveries is proposed by Starship Technology
(STARSHIP, 2018), which created an electric robot for deliveries in urban areas. A
small, six-wheel robotic delivery vehicle equipped with nine cameras, GPS, radar
and computer vision autonomously drives itself on the pavement to make small-size
deliveries (e.g. one cube). Currently, 150 such devices have been tested in Austria,
Belgium, France, Germany, the Netherlands, Spain, the UK and the USA for food
deliveries (e.g. Just Eat in the UK). It autonomously travels around the city, and
creates its own road maps using safer paths algorithms. When it gets a delivery
order and goes to the restaurant using GPS. Then it picks the food up at the
restaurant, and goes straight to the customer’s home. It travels on the pavement at a
4mph speed, sharing the space with pedestrians, which may cause issues for
accessibility. It is programmed for dynamic routing, so it can identify and select the
safest
25
and shortest routes. The vehicles are capable of automated travel, with
human oversight and assistance especially when dealing with unexpected events.
The load can be accessed only through a specific app or code that allow both
forwarder and receiver to open the cargo. Security is guaranteed by a control system
with a visual and audible alarm. The use of motor vehicles, including delivery robots,
on the footway is prohibited under Section 72 of the Highway Act 1835. This is
primarily to ensure the accessibility of the footway to pedestrians, particularly those
with mobility and visual impairments. The Government does not have any plans to
review existing regulations on the use of the footway at this stage.
The system provides an on-demand local delivery service, so it can be used to
reduce or avoid the number of failed deliveries from logistics operators, who attempt
to deliver with a high uncertainty of finding the recipients at home. Failed deliveries
account for 5% of total e-commerce deliveries: they cause high costs due to the
increased pollution caused by HGVs (e.g. trucks) and LGVs (e.g. vans), and loss to
e-commerce retailers (e.g. £183,132 for UK retailers in 2018 due to failed deliveries;
Taylor, 2018). The Starship technology aims to reduce costs (e.g. on-demand
service, electric robots, reduced LGVs and HGVs in urban areas), improve efficiency
(e.g. reduced and more accurate delivery time) and safety of last-mile deliveries. A
key challenge of this technology is social acceptance. Pedestrians have to share the
pavement space with it. It is an open question as to how they will react to this
technology. Starship co-founder and CEO Ahti Heinla declared that next year the
robot will be able to operate in dense pedestrian areas; there might be 1 million
robots operating around the world in five years’ time (Financial Times, 2018).
Urban deliveries are usually made by LGVs rather than HGVs. The UK Licence
Bureau declared that commercial vehicles create 20% of traffic. Three-quarters of
that traffic are vans, while the remainder are HGVs (Department for Transport,
2016b). Van traffic is growing faster than car traffic (+12% of vans versus +4% of
cars in 2014). This is probably due to increased e-commerce, which has generated a
high number of small-size deliveries in urban areas, making urban goods distribution
increasingly unsustainable. For this reason, new technologies might be seen a new
tool to urban delivery with the aim of reducing or avoiding the number of vans, so as
to make cities more sustainable.
25
It identifies safe or unsafe zones to minimise risks for itself and others.
New Technology and Automation in Freight Transport and Handling Systems
32
In general, despite the high benefits provided, some key problems occur with AVs for
urban goods distribution. Kunze (2016) identified the following issues:
• the need for loading and unloading systems or devices at both ends of the last-
mile transport, resulting in high costs
• low compatibility of delivery recipients at the destination location – e.g. drop
boxes, commercial unloading bays, etc.
• security and safety issues if no human is present – social norms expect that
goods will be accompanied by a responsible human, which may be a restraint
on speed of uptake
Drones. Another challenging solution is proposed by Amazon, which considers the
use of drones for small-size urban deliveries. Such systems have been
demonstrated already in some Chinese cities (Engelking, 2015), although they are
still subject to tight regulatory restriction in Europe and North America. Some
applications of drones have been undertaken with medical products deliveries in
developing countries, to overcome access problems caused by poor surface
transport infrastructure, or to remote communities in developed countries (Haidari et
al., 2016).
However, some limitations occur as a result of current drone technology capabilities.
For example, the Amazon ‘Prime Air’ octocopter has been estimated to have a
carrying capacity equal to 2.3kg and a maximum range of 16km (assuming empty,
therefore lightweight, return). This limits applicability to different types of deliveries in
terms of size, even though DHL (a) (2014) suggests the use of drones more within
areas that are geographically difficult to access (e.g. rural areas) rather than for
urban deliveries. Nevertheless, an extensive drone delivery scheme might be
supported through an alliance between local retailers and wholesalers with surplus
storage capacity available, to create a new network of local stockholding points –
thereby deploying drones for the final leg, for which vans are least efficient.
In 2015 Amazon trialled a Prime Air drone delivery service in Cambridge, UK. The
trial was open only to customers who lived close to the Amazon depot, and owning
sufficient private land to allow the drone to land. The order weight limit was set at
2.6kg. The delivery took 13 minutes from website order to drone arrival. The
demonstration was possible due to UK Government regulations allowing companies
to experiment with autonomous aircraft that fly beyond the line-of-sight of the
operator in rural and suburban areas. In the longer term, one operative might control
multiple largely autonomous drones, with system sense-and-avoid technology for its
autonomous capabilities (Hern, 2016).
On average, between 15 and 16 drones would be required to replace one van.
D’Andrea (2014) estimated that the direct operating costs of a drone are in the order
of $0.10 for a 2kg payload and 10km range, while a traditional system incurs costs
around $0.60 per item to be delivered. This makes deliveries with drones feasible
from a cost perspective. However, automation research – i.e. research on vehicle
design, localisation and navigation and vehicle coordination – is still needed to make
drone delivery practical.
New Technology and Automation in Freight Transport and Handling Systems
33
The main risks to the public include:
• safety – notably the risk of malfunctioning drones crashing onto people or
property, or collision with aircraft
• security – hacking of drone control systems for malicious purposes
• noise emissions
• low energetic efficiency
• impact on local ecology (e.g. on birdlife)
• lack of adequate near-ground air traffic regulation (D’Andrea, 2014; Whitlock,
2014; Kunze, 2016)
In general, Kunze (2016) argues that the adoption of urban air drone logistics on a
larger scale is unlikely because they are neither energetically efficient, nor will
citizens tolerate air drone noise emissions.
However, arguably even greater problems are related to both security and safety and
the business model. In the UK in recent months, in addition to concerns about air
proximity incidents at Heathrow Airport, there have been numerous negative media
reports about the use of drones, including their illegal use for the delivery of drugs
and other commodities to prison inmates, and concerns that even legitimate
business use would cause intrusive overflying of private property, where the concern
is as much the potential for surveillance as noise pollution.
Overall, it seems reasonable to suggest that the advantages of using drones in rural
areas, due to the limited extent of current freight transport and logistics systems,
combined with the reduced problems of interaction with existing activity and property
in lower-density areas, will see UK market development first in this context.
3D printers. Last-mile deliveries are increasingly related to e-commerce and its
business-to-consumer market, which generates a high level of lorry traffic in city
centres. According to Iwan et al. (2016), despite the global economic crisis, online
sales have shown significant growth over the last years, making the negative
impacts of last-mile deliveries also more significant. Business-to-consumer last-mile
deliveries are already considered the most expensive, least efficient and most
polluting leg of the whole supply chain (Gevaers et al., 2014). However, in the UK,
already the country with the greatest sales penetration of online retailing, the market
share for e-commerce is projected to grow from 13.8% in 2015 to 17.1% by 2020
(McKinnon, 2016).
An emerging solution to reduce urban freight flows is offered by 3D printing. This
allows consumers to 3D print products they have purchased in their own homes,
through a process of laying down many thin layers of a material (such as plastic) in
succession to make a physical object from a three-dimensional digital model.
According to Taniguchi and Thompson (2014), 3D printing and other emerging
technologies have the potential to reduce demand for goods movement, replacing
physical distribution with digital delivery of product (McKinnon, 2016). This follows
New Technology and Automation in Freight Transport and Handling Systems
34
the business model pioneered by software and music download providers, whereby
vendors sell reproduction licences for ‘printing’ instead of finished products. Keeney
(2016) estimates that the 2020 3D global market could grow to more than $40 billion,
and other analysts estimate a range from $12 billion to $490 billion for the next five
to ten years, as shown in Figure 10. Keeney argues that 3D printing potentially could
grow by more than 40% a year over the next five years. However, uptake to date by
individual consumers has not been high, despite the fact that the price for a basic 3D
printer is approximately $200 (McKinnon, 2016).
Figure 10: 3D global market by 2020
Source: Keeney (2016), Figure 2. Graphic reproduced with permission of ARK
Investment Management, LLC
According to Kunze (2016), the advantages of using 3D printing include:
• the possibility to customise products;
• reduction in freight-kilometres travelled in the upstream supply chain
26
, and
related reduction of polluting emissions, congestion and noise;
• reduction in lead time – important in the case of urgent deliveries, for example
a replacement part.
However, currently there are at least several barriers and limiters to uptake. A wide
range of material types are in use, but different 3D printers are required to handle
different materials e.g. plastics, metals and even food. Multi-material printers will be
developed in the future, but are not available in the mid term. There are also
uncertainties about the business model. While low-cost basic 3D printers might be
available, consumers will expect complex, multi-material products to be available if
26
The ‘upstream supply chain’ refers to suppliers, purchasers and production lines near the beginning
of the processes linking the production of goods and services with eventual consumers.
New Technology and Automation in Freight Transport and Handling Systems
35
they are to use the technology; but this will impact significantly on cost, suggesting it
is unlikely that private households will own multiple 3D printers (working with
different materials) in the mid-term future. Instead, local centres could pool different
printers and offer the opportunity to have 3D print-on-demand products.
Nonetheless, according to Kunze (2016), the potential of the 3D printing solution is
difficult to assess because it depends on the product development, production and
sales strategies of producers rather than factors controlled by the logistics sector.
For example, further innovations and improvements in material and printer
capabilities are needed to accelerate adoption and create new applications for
additive manufacturing (Keeney, 2016). Still, the technology is worthy of policy
attention because of its potential to reduce upstream transport and the related
negative externalities.
In general, Kunze (2016) suggested that all the above-mentioned innovative
solutions for the future of last-mile deliveries can be integrated into a ‘freight urban
system’ that includes local cross-docking centres for receiving and collecting goods.
This solution could involve consolidation centres, pick-up points and packstations,
where people can variously collect their goods themselves or receive them via larger
vehicles, crowd logistics
27
solutions, drones and other sustainable delivery solutions
(e.g. cargo bikes). In this environment, 3D printers can be integrated to reduce
freight flows due to e-commerce and home deliveries. The overall system also could
solve the problem of delivery failures that cause an increase in last-mile delivery
trips.
6. Disruptive business models
Implementing automated freight systems can be subject to a technology push or a
market pull approach. In technology push, market needs are not considered before a
technology is developed and pushed onto the market. At the moment, truck
platooning is based mainly on a technological push approach, as national and
international authorities are facilitating truck manufacturers to bring their technology
into the market. Some forecasts about market availability do indeed suggest that a
‘push’ strategy may be plausibly effective: Keeney (2017) expects autonomous
trucks to be commercially available within the next five years, and international
companies that are currently involved in developing autonomous truck platforms
include Tesla, Waymo, Uber, Volkswagen, Volvo, PACCAR and Daimler.
However, it is not just the availability of technology that influences take-up, and there
are reasons to suggest that the push strategy might not be sufficient. Conversely, a
number of societal and economic benefits come from the implementation of truck
platooning, such as reduction in transport costs, increased safety and decreased
congestion and pollution (Bakermans, 2016). For this reason, due to the high-value
27
Crowd logistics is beyond the scope of this technology-focused review, but involves individual
actors (citizens or businesses) being incentivised to deliver consignments where they can do so for
marginal cost: for example, to a house next door or in the next street which is on or very near to an
intended journey route.
New Technology and Automation in Freight Transport and Handling Systems
36
benefits, a market pull approach based on the needs of the stakeholders involved is
also needed.
Connected automated vehicle (CAV) technologies for freight applications are likely to
be early adopters of high-level automation (Shladover, 2017). However, Shladover
argues that new business models for CAV deployment based on innovative public–
private relationships need to be encouraged to lead vehicle–infrastructure
coordination and bridge investment gaps.
A pull factor for CAV adoption is also the main value proposition highlighted within
the COMPANION project: fuel consumption savings due to air drag reduction by
platooning. Savings of at least 5% of fuel have been quantified. However, a deeper
analysis should quantify savings by considering the total logistics costs. In Europe,
fuel represents 9–32% of total costs (including personnel, administration, capital
costs, taxes and insurance and other variable costs). If fuel is on average 20% of
total costs, it represents an important cost, in the sense that all carrier costs are
important in a highly competitive marketplace, but it is not dominant.
Indeed, in the short-term perspective, fuel saving would not be the most important
factor influencing a company that might be considering using platooning. In fact,
driver acceptance is held as one of the most critical factors for adoption. Driving in a
platoon must be perceived as safe and comfortable: hence, among the factors
influencing the success of platooning in the short term is education. Drivers need to
receive objective information and proper training and support to perceive platooning
as a safe system, in order to be able to accept it.
Another important factor is interoperability between platooning systems for lorries
produced by different automotive manufacturers, which is essential for the viability of
a platoon system. Transport companies want to be able to platoon with all their
vehicles, which often are sourced from different manufacturers.
At the moment AVs are prototypes rather than a part of standard production, and
therefore expensive. However, costs are expected to fall rapidly with further
development and mass production. KPMG (2015) estimate the additional costs in the
long run of full automation for commercial applications as low as £5,000 per vehicle
in the UK. Wadud (2017) estimated the costs and benefits of automation through
three different scenarios (Table 3). For example, the cost of equipping a 38-tonne
trailer truck ranges from £12,500 in his optimistic scenario, through to £20,000 with
his pessimistic scenario. In respect of savings, automation is expected to provide a
reduction in commercial driver labour costs ranging from 80% to 60%. In general, it
can be said that the benefits of automation are higher for commercial vehicles than
for cars, so it is worth adopting full automation earlier in the freight sector (Wadud,
2017).
New Technology and Automation in Freight Transport and Handling Systems
37
Table 3: Cost and benefit input of automated systems in different scenarios
Optimistic
(£)
Baseline
(£)
Pessimistic
(£)
Cost of automation
38-tonne trailer truck
12,500
15,000
20,000
18-tonne trailer truck
12,000
14,500
19,000
7.5-tonne trailer truck
11,500
14,000
18,000
Taxi
9,400
11,400
15,000
Private car
9,400
11,400
15,000
Driving time benefits:
Commercial driver salary
reduction
80%
60%
60%
Private car productive use of
time
60%
40%
25%
Fuel-efficiency benefits
10%
5%
5%
Source: Wadud (2017), Table 6
In the case of innovative solutions for last-mile deliveries, the definition and
assessment of business models is more complicated. Further research is needed for
both investment and operating costs, which can be assessed based on the specific
market potential of each solution. Different scenarios can be considered and
assessed only when further information about the likely business models are
available.
However, Keeney (2016) reports on a study to identify the beneficiaries of disruptive
technological innovations, such as 3D printing (shown in The same study pointed out
that “while the 3D printing manufacturing space will remain fiercely competitive, 3D
printed parts that are being designed into supply-chains should prove to be more
durable and more defensible than those in the consumer and prototyping spaces”.
Indeed, according to Keeney, prototyping is a well-established market, accounting
for $12.5 billion. However, 3D printing is also penetrating injection and cast-moulding
applications, opening up a potential addressable market of an additional $30 billion
globally. Different international companies (e.g. Airbus, Nike, Adidas, and GE) use
3D printing for direct product manufacturing, while Ford uses it for moulds and
prototypes, with plans eventually to 3D print finished products (Keeney, 2016). Such
future applications of 3D printing for finished products, are predicted to be an
additional $500 billion market (Figure 11).
New Technology and Automation in Freight Transport and Handling Systems
38
Table 4). The range of actors and processes involved reinforces the idea that this
remains a nascent technology.
The same study pointed out that “while the 3D printing manufacturing space will
remain fiercely competitive, 3D printed parts that are being designed into supply-
chains should prove to be more durable and more defensible than those in the
consumer and prototyping spaces”. Indeed, according to Keeney, prototyping is a
well-established market, accounting for $12.5 billion. However, 3D printing is also
penetrating injection and cast-moulding applications, opening up a potential
addressable market of an additional $30 billion globally. Different international
companies (e.g. Airbus, Nike, Adidas, and GE) use 3D printing for direct product
manufacturing, while Ford uses it for moulds and prototypes, with plans eventually to
3D print finished products (Keeney, 2016). Such future applications of 3D printing for
finished products, are predicted to be an additional $500 billion market (Figure 11).
New Technology and Automation in Freight Transport and Handling Systems
39
Table 4: Beneficiaries and benefits of 3D printing
Beneficiaries
Benefits
Software and modelling tool
providers
Should collapse the time and distance
between design and production
Innovative materials manufacturers
Will enable performance and form
factors that otherwise would be
impossible
Scanning and measurement
companies
Will help incorporate real-world
measurement as an important design
input for production
Service centres
Will help manufacturers transition
from traditional to additive techniques,
accelerating adoption of additive
techniques into design-cycles
Innovative manufacturers
Should enjoy competitive advantages,
as they provide customers with better
performance and more customised
products at a faster pace and cheaper
cost than their competitors
Source: Keeney (2016)
Figure 11: 3D printing global market penetration
Source: Keeney (2016), Figure 3, Graphic reproduced with permission of ARK
Investment Management, LLC
However, as noted in Section 3, multi-material 3D printers are not available at the
moment, and it might be difficult to print product made by materials with different
process requirements. For example, a product made by metal and plastic cannot be
printed by a 3D printer, because metal requires significantly higher temperatures
than plastic. For this reason, the materials need to be very similar in terms of
structural behaviour, and in this case there is still a need to change the print head
when shifting from one material to another.
New Technology and Automation in Freight Transport and Handling Systems
40
In summary, multi-material 3D printers are still in the experimental stage – even
when they can be used, they exhibit low reliability in terms of quality. This, together
with high equipment costs and long product realisation time, currently make 3D
multi-material printers a poor alternative to more traditional industrial printers.
7. Stakeholder requirements
Before analysing the stakeholders involved and their needs, it is worth clarifying the
difference between users and stakeholders: users are all parties that directly interact
with a system, whereas stakeholders are those parties that not only have an interest
in a system, but are also directly or indirectly affected by it.
Identifying the relevant stakeholders for automated transport systems is necessary to
identify the enablers of, and barriers to, automation for freight in an effective way.
Clarifying the interests of different stakeholders is important when seeking to
estimate the potential of new technologies.
For example, the main stakeholders involved in the adoption of truck platooning are
the following.
a. The carrier
This is a company responsible for transporting goods for a client (e.g. a consignor),
and is physically in charge of the transport. The carrier aims to complete transport
assignments according to agreed time constraints, at minimal cost. Carriers are the
key customers of automated and collaborative trucks (platooning), and business
models will target them throughout (Bakermans, 2016).
The literature shows that they are interested in this type of new technology and are
positive towards fuel-saving and safety-improving concepts in general. However,
some are worried about the added complexity and risk of delays due to the
challenges related to coordination between different carriers.
b. The shipper
This is the actor which owns the goods. Shippers generally contract a logistics
provider to transport their goods. This is the most important actor, because the
shipper can be considered the transport service’s customer. For example, in the
same way that currently, a shipper can require the use of low-emission trucks, it
could require carriers to use truck platoons for environmental or financial reasons
(Bakermans, 2016).
c. The truck driver
The driver is responsible for driving the truck from origin to destination according to
given time constraints, at minimal cost. Drivers are key actors because their
willingness to accept an automated system impacts on the time it takes to reach
market acceptance. The study carried out by Neuweiler and Riedel (2017) pointed
New Technology and Automation in Freight Transport and Handling Systems
41
out that some drivers are worried about risks and their own safety, in addition to the
impact of automation on the driver’s future role.
Conversely, several drivers expressed both interest and optimism regarding the
benefits of automated systems such as platooning. According to Bakermans (2016),
the truck drivers of the future will no longer be responsible for driving; rather, they will
conduct other tasks such as administration, with a related increase in role efficiency.
d. The regulator
National road authorities are responsible for supporting policymakers in defining
regulations to implement new technologies: they have a key role in the first stage of
implementing truck platooning. Regulations should take into account the impact of
platooning on road capacity, road safety and the environment. Collaboration
between different national road authorities is needed to avoid extra costs for cross-
border activities. Also, new assessment criteria and tools are needed to allow and
foster automated vehicles on public roads (Bakermans, 2016).
e. The systems provider
This category of stakeholder includes the vehicle manufacturer responsible for the
fulfilment of automated trucks, and advanced technology manufacturers responsible
for the sensor and cooperative control systems built into the assembled vehicles, to
enable their participation in platooning. The major truck manufacturers, such as
Scania, Volvo, MAN, Iveco, DAF and Daimler, are able to implement platooning
techniques in their trucks, and it is critical for cost reduction that they are integrated,
rather than after-market solutions.
Several pilot projects and tests have been developed (Bakermans, 2016). Truck
manufacturers are willing to invest in platooning because they are convinced of its
positive effects, and are generally positive towards platoon interoperability between
brands (COMPANION, 2016).
f. The policymaker
Policymakers are responsible for defining and implementing new regulations for new
technologies to be adopted in the freight sector. Due to the multiple societal benefits
predicted from automation and platooning of trucks, central governments may
choose to legislate to facilitate truck platooning by establishing specific operating
conditions, such as mandating particular technical standards which will ensure
interoperability. However, policymakers need to have a clear quantification of the
potential impacts of platooning to be convinced that the overall effects of policy
change would be positive, and that unintended or undesired consequences (such as
a possible reduction in the use of rail freight) are either unlikely or, in the event they
do arise, at acceptable levels.
In addition to those identified above, other important stakeholders have a central role
in the context of the business model for platooning (COMPANION, 2016).
New Technology and Automation in Freight Transport and Handling Systems
42
g. Driver training service providers
These are responsible for training professional truck drivers. Their role is very
important, especially in the short-term, because drivers will need somewhat different
knowledge and skills in order to be able to transition to autonomous/platooned
trucks.
h. Service providers of business support and education
systems
These offer support to carrier companies during the first stage of implementing
platooning. Their role is important during the short term and mid term of the transition
to platooning, as sources of information about the benefits of, and effective business
practices associated with, adoption.
i. System providers for platooning coordination
These are responsible for developing, distributing and maintaining the systems for
coordinating trucks into platoons. They may also be the system operators. Their role
will be increasingly important in line with the extent of adoption of automated
systems during the transition period, as these are the organisations that will have
deep technical knowledge of the control systems, their safe operation, and
appropriate responses in the case of unintended or unexpected incidents. It will be
these organisations that ‘train the trainers’ of drivers involved in platooning.
8. Enablers and barriers
The most likely enablers of, and barriers to, the adoption of new technologies in the
freight sector are highlighted in Figure 12.
New Technology and Automation in Freight Transport and Handling Systems
43
Figure 12: Enablers of, and barriers to, the adoption of new technology in the
freight sector
New Technology and Automation in Freight Transport and Handling Systems
44
a. Enablers
The availability of cutting-edge information and communication technologies
(ICTs) is a clear prerequisite to successfully adopting new automation technologies
in the freight sector. In particular, the provision of nationwide superfast/Gigabit
broadband and high-speed mobile networks will be fundamental to supporting all
aspects of the UK economy in the 21st century. With specific respect to the freight
sector, such high-capacity data transmission networks will unlock potential for:
• the development and deployment of V2V, vehicle-to-infrastructure and vehicle-
to-cloud communication technologies, as required for the automation of road
haulage vehicles
• increased capacity to transmit logistics data between freight providers (and to
customers), to improve efficiency in freight operations
• the operation of automated drones for last-mile deliveries
Currently, logistics providers use a mix of different data structures. Standardised
data structures and transmission protocols will be required to maximise the potential
for data sharing across the freight sector. ICTs might enable supply-chain managers
and, in general, all the actors of the supply chain (e.g. shippers, logistics operators,
retailers, end consumers) to improve performance of their activities by virtue of more
efficient information flows. Such a development would improve the overall quality of
the services provided across the supply chain. Emerging solutions such as the block-
chain
28
or e-market platforms (or port community system platform for the shipping
sector) also might affect the introduction of automation in different logistics
industries, making automation more attractive to potential investors due to the
provided high integration of automated systems to the supply chain.
Companies that provide ICT services gain an essential role in this case, being
responsible for designing and offering up-to-date systems to improve the
competitiveness of the supply chain.
Compelling and reliable evidence of supply chain efficiency gains and cost
reductions is also necessary to incentivise private-sector stakeholders (e.g. supply-
chain managers, automotive sector, logistics operators) to invest in new
technologies. An analysis by Keeney (2017) suggests that autonomous electric
trucks could be expected to reduce the cost of trucking from $0.12 per ton-mile to
$0.03 per ton-mile during the next five to ten years – an operating cost reduction of
75%. Such significant operating cost savings will offset the higher capital costs of
emerging technologies (Figure 14).
However, estimates such as these need to be supported by real-world trials to
validate the claims, which are likely to need state support so that financial risks are
28
“Block-chain is a digital database containing information (such as records of financial transactions)
that can be simultaneously used and shared within a large decentralized, publicly accessible network;
also : the technology used to create such a database” – Merriam-Webster, https://www.merriam-
webster.com/dictionary/blockchain.
New Technology and Automation in Freight Transport and Handling Systems
45
shared between public and private bodies. Findings such as the cost savings
identified by Keeney (2017) also need to be effectively communicated to key
stakeholders across the freight sector, to demonstrate the potential benefits of the
new technologies.
Figure 13: Electric vehicle savings and autonomous vehicle savings
29
Source: Keeney (2017), Figure 1, modified graphic reproduced with permission
of ARK Investment Management, LLC
Figure 13 shows costs savings with autonomous and electric trucks. The most
important benefits arising from AV adoption are labour cost savings and higher
vehicle utilisation (i.e. they are used for a greater portion of available hours).
Moreover, if the vehicle is electric, there are modest additional net savings, mainly
due to (currently) higher drive-chain costs (the parts of the vehicle which make it
move) being offset by lower energy costs. There is a 0.01 cent cost saving per US
ton-mile that can be attributed to both electric vehicles and AVs. Electric vehicles
lower operating costs, and when the autonomous mode is turned on, operating
29
Red indicates the additional costs due to electric and autonomous trucking, while green indicates
cost savings regarding traditional trucking, if the electric-automated mode is used. Both are expressed
in additional or reduced $ per US ton-mile.
New Technology and Automation in Freight Transport and Handling Systems
46
savings are magnified. Notably, the study carried out by ARK reflects the US context.
In Europe it might be different, because road traffic conditions on the long haul in the
USA (e.g. long distances, low congestion) are such that AVs can maximise their
performance and the benefits can be fully exploited. It is also worth noting that diesel
is more expensive in Europe, so probably more significant savings can be achieved
in European countries by conversion to electric power. However, it is not easy to
make assumptions about the difference on cost-per-ton mile due to uncertainty
around future regulations (e.g. tax on electricity used for transport, distance-based
charges).
Figure 14 shows the future prospects of freight transport costs by mode.
Autonomous electric trucks are compared with other means of transport, suggesting
that they would be significantly more competitive. As in the case of Figure 13, this is
based on a US study. The equivalent analysis for Europe might be even more
positive, as the cost of transport by train is higher in Europe than in the USA, making
AVs potentially more competitive in the UK rather than in the USA, although with
possible negative consequences for rail freight.
Figure 14: Cost per ton-mile by mode of transportation
30
Source: Keeney (2017), Figure 2, graphic reproduced with permission of ARK
Investment Management, LLC
Significant costs savings can be achieved also with remote and automated shipping.
According to Rolls-Royce (2016) there might be direct (e.g. mainly related to the
vessel – improved efficiency of space in ship design, crew, fuel consumption) and
indirect (e.g. mainly at company and network in the shipping sector – optimised
operations and processes, improved overall performance, reduced human errors,
improved safety and service quality) benefits. These benefits could provide the UK
shipping sector with a key tool to gain long-term competitive advantages from
30
cost per ton-mile for air and barge is using 2014 and 2011 data, respectively (latest available). EV –
electric vehicle.
New Technology and Automation in Freight Transport and Handling Systems
47
autonomous shipping. In particular, according to Opensea.pro (2017), having no
crew on board would:
• drastically reduce operating costs – e.g. crew living expenses are more than
50% of a vessel’s operating costs, resulting in a saving of about $1 million a
year for handy-size bulkers, and $1.5 million for handy-size product tankers
• increase revenue – e.g. more space on board for loading cargo; till $0.50
million higher revenue a year for a vessel’s deadweight increased by 10% (e.g.
as estimated for a handy-size vessel performing one Indonesia–India trip per
month)
• reduce voyage expenses due to the reduced weight – e.g. 5% lighter (Rolls-
Royce, 2016), till $0.30 million per year for a handy-size bulk carrier that sails
for about 250 days per year
Another noteworthy advantage is reduction of costs due to piracy: autonomous
vessels will be difficult to board, and the absence of a crew which could be held
hostage would discourage pirates. Assuming effective cybersecurity, on-board
control of the ship could be made unavailable and immobilised through remote
control, making it relatively easy for naval authorities to reach it.
b. Barriers
Conversely, the absence of infrastructure, including ICTs and the complementary
roadside systems required to enable operation of AVs (signals, signs and sensors)
will hold back innovation in the freight sector; as will the lack of an appropriate
regulatory framework for the use of automation technology. Indeed, it is the
absence of an appropriate legal framework that is constraining the large-scale
deployment of automated trucks and platooning, rather than technological problems
per se.
In the early stages of technological development, there may be limited added value
associated with taking on new technologies, given their high capital costs. Hence,
early adopters need to be incentivised (e.g. through financial support) to take on
such risks. The distribution of cost savings also needs to be explicitly considered.
For example, in the case of platooning, it is of course the case that the ‘following’
vehicles save fuel and there is no benefit to the leading vehicle in this regard, while
there may be additional operating costs or liabilities, particularly if the lead vehicle is
human-driven. In such circumstances, it may be necessary to introduce a pricing
mechanism to ensure that benefits are evenly distributed across all parties involved
in the platoon.
Linked to the requirement for compelling evidence of the benefits and reliability of
new technologies, collective beliefs and normative views about appropriate modes
of operation held within institutions could act as a brake on innovation. For example,
in relation to the use of platooning in road haulage, evidence from the COMPANION
(2016) project indicated that negative perceptions among drivers could present a
barrier to its adoption by firms. Drivers were found to perceive that platooning is
unpleasant and uncomfortable; platooning limits drivers’ ‘freedom’; and manual
driving is better and safer than automated operation. Also, drivers might be worried
New Technology and Automation in Freight Transport and Handling Systems
48
about future employment, as in a fully automated freight future their jobs would either
radically change or disappear altogether. Opposition from drivers might represent a
significant barrier to implementing automated systems, as their presence is required
in the transition period from Level 1 to Level 5 automation (see Section 3.3.1).
The same problem could occur with warehouse employees. The long-term vision is
that the warehouses of the future will be fully automated. This would improve overall
logistics performance notably (e.g. reduced time, error and cost, and improved
safety), but would significantly reduce the number of humans employed in the sector.
These people might show opposition to new technologies and automation due to the
high impact that these have on the employment sector.
Furthermore, new skills are not easy to acquire and their transfer takes time, while
the current labour force might resist change. A national policy to manage the
consequences of the adoption of automation across the economy might be a
top-down option for reassuring workers that they can be net beneficiaries from this
revolution. Clearly, convincing evidence that automation technology is safe – and
indeed, safer than manual operation – is required to overcome early misperceptions
concerning risk. High-profile (if low-probability) safety issues arising during trials of
AVs (as happened with the death of a driver using a Tesla vehicle equipped with
autopilot; Tesla, 2016) could have long-lasting impacts on the prospects of adoption.
A comparative lack of economies of scale for new technologies also puts them at a
competitive disadvantage to current modes of operation. To give two examples, for
most consumer products, 3D printing of individual items will have much higher unit
costs than conventional batch production, while the delivery of individual parcels by
drone will be costlier than their distribution in consolidated loads on multiple drop
rounds by vans. Consumer-based 3D printing and drone distribution will also require
a substantial capital investment, in the latter case on landing stations as well as
unmanned aerial vehicles. This is also likely to deter many potential users.
Nevertheless, even if economies of scale that depend on new network elements
might be difficult to achieve, especially in the short to medium term (due to high
infrastructure investment costs), efficiencies might still be achieved by improving
operating practices, for example, by optimising routes taken through the network).
Therefore, it is important to establish mutually-beneficial mechanisms that enable
logistics companies to collaborate and share best practice. Smaller companies
with less buying power will need support (e.g. either financially, or through the
provision of training) to enable them to alter their operating practices.
9. Building the future
Neuweiler and Riedel (2017) identified that the majority of the research projects
carried out in this field focus primarily on the technical aspects of autonomous
driving, rather than on the impacts and implications of the adoption of automation.
They analysed 399 papers on the topic of autonomous driving, which mainly focus
on technological development (91.2%), and few covering user acceptance (1.3%),
regulations (1.5%) and environmental impacts (1.3%).
In sum, as described in the previous sections, high potential benefits would come
from automated systems for freight. However, there is a lack of research to estimate
New Technology and Automation in Freight Transport and Handling Systems
49
the overall economic benefits. For the most part, the research which has considered
economic benefits has investigated the benefits of fuel consumption reduction, rather
than the social and environmental benefits. Nevertheless, despite the uncertainties
about and unknowns of this challenging topic, it is worth noting that both industry and
governments are willing to experiment and invest (Tavasszy, 2016).
a. Implications: a macroeconomic perspective
Automated systems could solve problems with the current transport system. In
theory, fully automated driving could double existing average road infrastructure
capacity through exploiting autonomous systems’ safety margins to the limit: for
example, in terms of following and passing distances, and by smoothing traffic flow.
In addition, for a given traffic level, connected and autonomous operation could
contribute to achieving environmental policy goals (reduced fuel consumption), and
significantly reduce collisions.
However, there are still important uncertainties about legal responsibility in the event
of a collision (Flämig, 2016). The transition to remote-controlled and autonomous
vessels will produce industry disruption, with impacts on shipping business
operations and a change of role for some shipping actors, generating high
uncertainty as to the future of shipping market competitors: new technologies might
be beneficial for big players (e.g. increased integration of global companies,
optimisation of fleet), while detrimental for smaller operators. Conversely, new
players will enter the market due to the creation of new services for digitalisation and
autonomous technologies, as is happening in the automotive sector.
b. Implications for sector employment and inequalities
Automation is a key way in which companies can compete through increased
efficiency in freight transport and logistics operations. However, as Lawrence et al.
(page 17, 2017) argue, automation is “likely to change the shape of the labour
market, the occupations that individuals work in, and the type of work tasks humans
perform”. Considering that the nature of work will change due to automation, there is
a need to identify the specific activities that will also change. McKinsey (2017)
argues that fewer than 5% of all jobs can be fully automated with existing
technologies, but that 60% of occupations have at least 30% of constituent activities
that could be automated today.
A large number of different jobs are likely to be affected by automation (Figure 15),
but some sectors are more susceptible to automation than others (Figure 16). This is
the case for occupations related to transport and storage, which are among those
more susceptible to automation.
New Technology and Automation in Freight Transport and Handling Systems
50
Figure 15: Automation potential among UK occupations
31
Source: Lawrence et al. (2017), Figure 1.7
The analysis carried out by Lawrence et al. suggests that automation may be
responsible for future growth in socio-economic inequalities due to the different
education and skill levels of employees in roles more or less susceptible to
automation. In short, workers with lower levels of skill are more at risk of complete
automation of their jobs. In fact, 46% of workers with a low level of educational
qualifications in the UK risk being replaced by automated systems, and the
wholesale, retail, transport and manufacturing sectors have a large proportion of
occupations that are technically susceptible to automation. The percentage falls to
12% for workers with undergraduate degrees or higher. This implies high impacts on
the inequalities that already exist in terms of future salaries and income in the UK
population.
According to the Cabinet Office (2017), automation in the UK could threaten gender
equality, as role-automation is more of a risk for women (46.8% compared to 40.9%
of traditionally male roles are potentially replaceable by automated systems), and
inequalities between ethnic groups (as some ethnic groups, e.g. Black, Pakistani and
Bangladeshi, are overrepresented in low-skill jobs). The implications for the freight
sector would seem to be to redouble efforts to ensure equal opportunities,
particularly if the nature of roles which are not automated will also change into the
future (Table 5).
31
The figure presents estimates of the proportion of jobs which could feasibly be automated within
different industries and regions. Analysis carried out by Lawrence et al. (2017) using the Quarterly
Labour Force Survey (ONS 2017d, 2015–16 data) and Frey and Osborne’s probabilities of
computerisation (2013). (See Lawrence et al., 2017 and Annex for methodology.)
New Technology and Automation in Freight Transport and Handling Systems
51
Figure 16: Percentage (horizontal axis) of roles in different UK industrial
sectors with the technical potential for automation
32
Source: Lawrence et al. (2017), Figure 1.8
32
The figure presents estimates of the proportion of jobs which could feasibly be automated within
different industries and different regions. Analysis carried out by Lawrence et al. (2017) (See
Lawrence at al. (2017) and see Annex for methodology.)
New Technology and Automation in Freight Transport and Handling Systems
52
Table 5: Industries with high numbers of jobs with the highest technical
potential for automation and low qualification levels among the workforce
Industry
Proportion of
jobs with high
potential for
automation
(p>0.7)
No. of jobs with
high potential
for automation
Proportion of
workers
without NVQ
Level 4
qualification
(%)
Wholesale,
retail, vehicle
repair
63.7
2,638,000
76.3
Transport and
storage
57.7
912,000
78.8
Accommodation
and food
service
64.7
1,093,000
78.0
Manufacturing
48.5
1,453,000
67.6
Source: Lawrence et al. (2017), Table 1.1
Driving activities will become less important with automation, and it is likely that
drivers will be expected to have higher qualifications to perform new administrative
tasks or technical tasks (e.g. monitoring of the autonomous truck) (Neuweiler and
Riedel, 2017).
However, the prospect of job losses does need to be put in the context of an
international shortage of truck drivers. For example, the American Trucking
Association estimates that this will grow from 48,000 vacancies in the middle of the
current decade, to 175,000 available posts in 2024 (Costello and Suarez, 2015).
Although of smaller magnitude, the same problem also exists in the UK, so to some
extent automation can be understood in this specific sector as solving a labour
shortage problem.
Moreover, in respect of the short run, the American Transportation Research
Institute (2016) has observed that during the first period of implementation, low-level
(1–2) AV trucks such as those used in platooning, will increase current drivers’
flexibility and improve their productivity, while they will still take over control when
needed, check lateral movement, handle paperwork, and manage loading and
unloading operations. Conversely, fully automated trucks (e.g. Level 5) would reduce
the need for drivers, but experts estimate that this level of automation is unlikely to
be commercially available for 15 to 20 years (Ginsburg and Uygur, 2017). Even then,
the American Transportation Research Institute (2015) suggests that limiting factors
will mean ‘last-mile’, final step in the chain, deliveries continue to be made by trucks
driven by human drivers, with automation restricted to the main, long-haul portion of
the chain.
Lower-level automated trucks may reduce the stress and monotony of driving for
long hours, and enable drivers to be involved in other activities during the trip, while
at the same time altering training needs (Ginsburg and Uygur, 2017). For this
New Technology and Automation in Freight Transport and Handling Systems
53
reason, the role of automated truck operatives is expected to appeal more to
younger jobseekers, addressing the issue of an ageing driver workforce (in the USA,
a median age of 52 years in private companies has been identified) (American
Transportation Research Institute, 2015).
c. Implications for the trucking industry and logistics
service providers
In 2016, the American Transportation Research Institute assessed the ‘top US
trucking industry issues’ as those identified in the left-hand column of Table 6. The
right-hand column summarises the benefits that were identified as potentially brought
by autonomous technology.
Table 6: List of top trucking industry issues and key autonomous truck
benefits
Top issues
Benefits
Hours of service
Allows for driver rest and productivity to occur
simultaneously
Compliance, safety,
accountability
Will decrease raw safety management system
scores, although percentile scoring needs to
change
Driver shortage
Driving is more attractive with higher
productivity, less time away from home, and
additional logistics tasks; fewer drivers may be
needed
Driver retention
Companies with autonomous technology may
attract and retain drivers
Truck parking
If ‘productive rest’ is taken in the cab during
operations, less time will be required away
from home at truck parking facilities, and fewer
facilities will be needed
Electronic Logging Device
Mandate
Modifications will be necessary, depending on
the level of autonomy
Driver health and wellness
Driver could be less sedentary; injuries could
be reduced
The economy
Carriers that use autonomous trucks may see
productivity and cost benefit
Infrastructure/congestion/
funding
Urban congestion could be mitigated through
widespread use of autonomous vehicles
(including cars
Driver distraction
Drivers will not be distracted if the vehicle is in
autonomous mode
Source: American Transportation Research Institute (2016), Table 10
Automation may affect a logistics service provider according to its size. In particular,
several logistics operators and experts interviewed by Neuweiler and Riedel (2017)
New Technology and Automation in Freight Transport and Handling Systems
54
declared that small-or medium-sized transport companies need to find niche
segments to stay in the market. Conversely, large companies can benefit from
optimisation and lower operating costs, even though companies need to be able to
afford the initial investment. For this reason, larger companies are likely to be the
drivers of innovation, while the smaller ones will follow. However, even if future
logistics are fully automated, there will still be a need for logistics services providers
(such as fourth-party and fifth-party logistics) to provide customer assistance.
Furthermore, most participants in the study conducted by Neuweiler and Riedel
(2017) believe that automated systems will imply the need for new services, such as
“maintenance services, platform services and platooning service providers,
loading/unloading support, document management, breakdown services, hazardous
freight handling, IT services and cloud services”. Most experts stated “value-adding
services” as source of “competitive advantages”.
d. Implications for the environment
According to Bakermans (2016), the implementation of truck platooning will imply a
reduction in fuel consumption, with pollutant emissions reduction directly related to
the volume of fuel used. For this reason, it can be argued that implementing truck
platooning would have a positive environmental impact, although it would be subject
to establishing what other changes in freight flows might occur, such as a possible
reduction in highly energy-efficient rail freight.
As discussed, last-mile deliveries are the most complex environment to evaluate due
to the complexity of the network, high number of stakeholders involved and human
presence. Due to the characteristics of traffic in urban areas (e.g. stop–start,
congestion) and the pollutant character of HGVs and LGVs, last-mile deliveries are
majorly responsible for air pollution and noise. This results in a strong impact on
public health due to the presence of humans in large numbers, who are exposed to
various emissions. For this reason, urban deliveries are one of the major causes of
unsustainability in terms of environment and human health. Noteworthy positive
impacts could arise from the adoption of innovative solutions for last-mile deliveries,
to reduce the flows of HGVs and LGVs and make good urban distribution more
efficient. However, according to Kunze (2016), there is a need to define the
methodology to assess urban ecological impacts in terms of air polluting emissions,
noise and congestion. In general, review of the current studies suggests that electric
AVs and platooning are more deliverable and important to ensure better air quality.
Drones could be useful in urban areas, but as previously mentioned, there are major
barriers related to safety, security and noise emissions.
A summary of implications is shown in Figure 17.
New Technology and Automation in Freight Transport and Handling Systems
55
Figure 17: Implications of automated systems and new technologies
e. What the ‘freight future’ might look like in the UK
This review has pointed out the advantages and disadvantages of automated freight
systems, and the enablers of, and barriers to, their implementation. Despite
significant breakthroughs in terms of technological development in recent years, fully
applying and adopting automation in the freight transport of the future remains
uncertain. Operational and strategic changes in the freight sector require new
solutions that provide clear and sure benefits to the actors involved across the
supply chain (e.g. companies, logistics operators, shippers, customers, etc.). Today,
companies’ competitiveness is based mainly on the efficiency of their supply chains,
which have potential to be improved with automated systems. However, supply-
chain sectors (e.g. warehouses, ports, railways, road transport, etc.) are
characterised by different degrees of automation development, which makes the task
of imagining the shape of the future more difficult. For these reasons, the authors
believe that there might be different ‘freight futures’ depending on the environment of
application.
New Technology and Automation in Freight Transport and Handling Systems
56
Maritime transport
In the maritime case, both shipping and ports could be automated. For shipping,
autonomous vessels could be remotely driven, coordinated, controlled and
monitored to reduce the high costs associated with human resources and improve
overall efficiency. For ports, high-level automation is likely to show more rapid
adoption, because there are some automated handling systems which have been
implemented already. Full automation might make UK ports more efficient and
competitive, resulting in reduced operational times and negative externalities
produced in the port environment (e.g. air polluting emissions, congestion due to
queue of trucks accessing the gate, etc.). For example, automated control of
information flows and physical flows of goods would allow for more efficient handling
of movements in and out of terminals. Combined with standardised cargo handling
systems, these innovations will enable terminals to optimise ship hosting.
33
Automated lorries would access the automated gates of the terminal, be
automatically checked and automatically exit. The full automated process would
make ports internationally competitive, not only due to time savings but also due to
the high-quality service offered to customers (e.g. track-and- tracing) and overall cost
reduction (e.g. safety improvement, reduction of number of errors, reduced labour
costs). Such developments would allow the UK to be considered among the leading
world players as a transhipment point between Europe and the USA, while also
being a reference logistics point for the Asian market.
Railways
New technologies and automation will support the railway system to become more
competitive in respect to other means of transport. In fact, ICTs and automated
transhipment systems will reduce intermodal
34
transfer times – one factor
responsible for making rail freight less competitive today. As mentioned previously, it
is worth noting mid-term future potential to utilise existing and planned high-speed
lines as ‘rolling motorways’. However, based on a holistic approach and looking at
the freight transport system as a whole, planning at the outset, capacity
management and developing transhipment points to efficiently integrate rail with road
are required.
Autonomous vehicles
AVs and CAVs might require more time to be commonly used for freight transport on
the long haul, due to lack of regulation and full technological development. In the
long-term future, when the technology is ready, platooning and automated trucks will
improve road safety, thanks to reduced rate of road collisions. They will also allow
reductions in per-unit energy consumption and air pollution.
33
Cargo Handling systems are mechanical equipment used for the movement, storage, control and
protection of materials, goods and products throughout the process of manufacturing, distribution,
consumption and disposal
34
The time costs of changing mode of transportation (e.g. road to rail, road to shipping, etc.)
New Technology and Automation in Freight Transport and Handling Systems
57
However, these results will be possible only if all modes of transport in the freight
sector are integrated, by improving transhipment and modal shift
35
. For example, if
road freight automation were to result in all goods being transported by road (due to
the high value benefits coming from automated road systems), then the transport
system might collapse due to greater demand for road space, which might cause the
opposite overall effect to the one intended, particularly if automation in the
passenger car sector, using the same infrastructure, has similar effects. A holistic
approach is needed to integrate different automated means of transport into an
inclusive freight transport system. To this end, a ‘successful freight future’ will require
policymakers’ support in terms of designing effective policies to push the
development of automated systems, and coordination among different modes of
transport.
Automated systems
Fully automated systems for last-mile deliveries might replace more traditional
delivery settings (e.g. trucks or vans), but it might not be until very far into the future
that they become widely used. This is due to their complexity (e.g. different actors
and products, integration with the urban transport system, etc.), which does not
always make automated systems suitable to delivery needs, and level of
technological development, which in the mid-term will not be sufficiently reliable.
Small automated vehicles or pods might be adopted more easily, if specific policies
(e.g. regulations, insurance, safety and security, etc.) are forthcoming. 3D printers
also might require more time in terms of technological development, and even then
may not prove to be commercially viable propositions: multi-material 3D printers
might not perform as industrial printers in the mid to long term, and may require a
long time to become a valuable alternative to the production processes such as
moulding and stamping that they seek to replace.
Drone technology
Drones could be used in a short- to medium-term period within a warehouse to
replace almost all the humans who work there, making warehousing much more
efficient than it is now (e.g. reduction in error, time and cost, with improvements in
safety). However, they might not be easily used for last-mile deliveries due to their
noise outputs and limited citizen approval. In the city of the near future, last-mile
deliveries will remain a challenge because a unique solution to make them more
efficient does not exist. However, the long-term future city might have an automated
system integrated with a more traditional one that is able to support humans to make
deliveries (with AVs), reduce the number of commercial vehicles (because
customers might 3D print the products that they buy in their own homes or at
common shared 3D printing locations), and make cities more sustainable, with
improved quality of life.
35
Modal shift means replacing a saturated means of transport with another to make the first less
congested.
New Technology and Automation in Freight Transport and Handling Systems
58
10. Recommendations
As suggested in the discussion of enablers of, and barriers to, innovation provided in
Section 8, national government potentially has a role to play in contributing to
creating an environment that stimulates innovation in the freight sector. At a high
level, an integrated package of measures that we identify as being part of this future
environment, and which would involve government as a facilitator alongside other
stakeholders are as follows:
1. Continued investment in nationwide, high-speed, high-capacity data transmission
networks (both fibre-optic and mobile).
2. Ensuring that legislative and regulatory frameworks are adapted to enable the
use of AVs on the public highway network. This includes giving due consideration
to standards for vehicles, roadside infrastructure and the regulation of AV
operation on public highways. Work is already underway in this space. This
includes the 2018 Automated and Electric Vehicles Act, the law commission
project on automated vehicles (2018) and developmental work at UNECE.
3. Ensuring that future rail freight strategy considers the potential deployment of
‘rolling motorways’ on new sections of the rail network, along with complementary
transhipment points, as is happening on the European continent.
4. Supporting and fostering the implementation of automated and innovative
systems to reduce the number of commercial vehicles in urban areas, and
improve the liveability of cities. This is a challenging point, due to the complexity
of the urban environment (e.g. involved stakeholders, high presence of human
beings, complex traffic), so it is highly recommended to undertake a consistent
and integrated package of actions to:
(a) investigate both general public and expert stakeholder perceptions and
acceptance.
(b) understand how to integrate new technologies efficiently in the current
system during the transition period;
(c) design a specific regulatory framework to promote a safe, environmentally-
friendly and efficient urban context.
(d) develop a strategic plan to support the private sector to adopt and develop
new systems of freight handling and movement. This includes provision of
financial support for research and development programmes, with evaluated
trials (to generate compelling evidence of efficacy), and training programmes to
increase workforce capacity regarding the adoption of new operating practices.
The centre for connected and autonomous vehicles are already undertaking some
work and thinking in this space, around some of the recommendations in 4.
In addition, beyond these more coordination and planning roles, we note that
government may wish to give consideration to more strategic and fundamental
regulatory legislation, with a view to automation being adopted in the safest, most
New Technology and Automation in Freight Transport and Handling Systems
59
ethically aware and socially just way. The process also might address the debate as
to whether a new national authority on robot and artificial intelligence ethics might be
necessary. A possible remit of such a body might be to ensure human safety in
proximity to autonomous technologies, to reflect on liability issues in cases where
autonomous technologies fail, and to advise on equitable strategies to deal with
circumstances in which human labour is replaced by automated technology.
Alongside an ongoing national context which fosters innovation, there is also a need
for further primary research into the issues identified in this report, with a focus on
the UK context.
36
First, it would be beneficial in particular to undertake an in-depth
analysis of the UK supply chain (considering different sectors, e.g. automotive, food,
coal) to identify prospects for the applicability of different automated freight systems.
This could be carried out at regional and national levels, giving consideration to the
major freight corridors. Second, it is necessary to develop enhanced models of, and
performance measurement tools for, freight transport systems. The data generated
by such tools could feed into decision support systems designed to appraise the
most effective policy packages, through evidence-based, cost–benefit assessments,
to stimulate automation in freight transport.
36
This study has been limited to a desk review of existing evidence, and it is acknowledged that a
large number of studies and trials have been carried out in the USA. This has a different operating
environment to the UK, characterised by a specific landscape, remote communities and major open-
cast mining technologies.
New Technology and Automation in Freight Transport and Handling Systems
60
11. References
Alessandrini, A., Campagna, A., Delle Site, P., Filippi, F. & Persia, L. (2015).
Automated vehicles and the rethinking of mobility and cities. Transportation
Research Procedia 5, 145–160.
American Transportation Research Institute (2016). Identifying autonomous
vehicle technology impacts on the trucking industry, November [online]. Available at:
http://atri-online.org/wp-content/uploads/2016/11/ATRI-Autonomous-Vehicle-
Impacts-11-2016.pdf [January 2018].
Anderson, J. M., Nidhi, K., Stanley, K. D., Sorensen, P., Samaras, C., &
Oluwatola, O. A. (2014). Autonomous vehicle technology: A guide for policymakers.
Rand Corporation.
Arup (2016). Future potential for modal shift in the rail freight market [online].
Available at: https://www.arup.com/publications/research/section/future-potential-for-
modal-shift-in-the-uk-rail-freight-market [January 2018].
American Trucking Associations (ATA) Technology and Maintenance Council
(2015). Automated Driving and Platooning Issues and Opportunities. Arlington, VA:
ATA Technology and Maintenance Council).
Bagloee, S. A., Tavana, M., Asadi, M., & Oliver, T. (2016). Autonomous vehicles:
challenges, opportunities, and future implications for transportation policies. Journal
of Modern Transportation, 24(4), 284-303.
Bakermans, B.A. (2016). Truck platooning: enablers, barriers, potential and
impacts. Master’s thesis, TU Delft [online]. Available at:
https://repository.tudelft.nl/islandora/object/uuid%3A3e4e265b-b84c-474b-85b9-
9840a5c85464 [November 2017].
Bagloee, S. A., Tavana, M., Asadi, M., & Oliver, T. (2016). Autonomous vehicles:
challenges, opportunities, and future implications for transportation policies. Journal
of Modern Transportation, 24(4), 284-303.
Besselink, B., Turri, V., van de Hoef, S.H., Liang, K.Y., Alam, A., Mårtensson, J.
& Johansson, K.H. (2016). Cyber–physical control of road freight transport.
Proceedings of the IEEE 104, 1128–1141.
Cabinet Office (2017). Race disparity audit [online], October. Available at:
https://www.gov.uk/government/publications/race-disparity-audit [January 2018].
Capacity4Rail (2018). About [online]. Available at: http://capacity4rail.eu/about
[December 2017].
CargoBeamer (2017). How it works in a CargoBeamer terminal! [online].
Available at: http://www.cargobeamer.eu/How-it-works-849768.html [November
2017].
New Technology and Automation in Freight Transport and Handling Systems
61
Celent (2012). A scenario: The end of auto insurance [online]. Available at:
https://www.celent.com/insights/121822340 [January 2018].
Cepolina, M. (2014). Urban Freight Transport: The FURBOT Delivery System,
final report [online]. Available at:
https://trimis.ec.europa.eu/sites/default/files/project/documents/20140129_133044_7
2144_URBANFREIGHTTRANSPORT_THEFURBOTDELIVERYSYSTEM.pdf
[November 2017].
Chan, E. (2014). SARTRE automated platooning vehicles. Towards Innovative
Freight and Logistics, 137-150.
COMPANION (2016). D2.7 – Documentation of the exploitation of the results.
European Commission, 27 September [online]. Available at: http://www.companion-
project.eu/wp-content/uploads/COMPANION-D2.7-Documentation-of-the-
Exploitation-of-the-Results.pdf [November 2017].
Costello, B. & Suarez, R. (2015). Truck Driver Shortage Analysis 2015,
American Trucking Associations (ATA) [online]. Available at:
http://www.trucking.org/ATA Docs/News and Information/Reports Trends and
Statistics/10 6 15 ATAs Driver Shortage Report 2015.pdf [November 2016].
D’Andrea, R. (2014). Can drones deliver? IEEE Transactions on Automation
Science and Engineering 11, 647–648.
Davies, M. (2014). Timetable advisory system (TAS) launched, Clyx.net, 6
February [online]. Available at:
https://www.clyx.net/Clyx/ClyxPortal/Home/Article/128 [[November 2017].
Department for Transport (2015). The Pathway to Driverless Cars: Summary
report and action plan, February [online]. Available at:
https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/40156
2/pathway-driverless-cars-summary.pdf [January 2018].
Department for Transport (2016a). Rail freight strategy – Moving Britain ahead.
London: Department for Transport.
Department for Transport (2016b). Road Use Statistics – Great Britain 2016 [online].
Available at: https://www.licencebureau.co.uk/wp-content/uploads/road-use-statistics.pdf
[February 2018].
DHL (a) (2014). Logistics Trend Radar: Delivering Insight Today. Creating Value
Tomorrow. Troisdorf: DHL Customer Solutions & Innovation.
DHL (b) (2014). Self-driving vehicles in logistics - A DHL perspective on
implications and use cases for the logistics industry. Troisdorf, Germany. DHL
Customer Solutions & Innovation.
DP World (2017). DP World London Gateway reduces its carbon emissions by
28% in 2016, 25 September [online]. Available at:
http://www.londongateway.com/news-media/news/1437 [February 2018].
New Technology and Automation in Freight Transport and Handling Systems
62
Engelking, C. (2015). Drone delivery services are booming in China. Discover,
27 March [online]. Available at:
http://blogs.discovermagazine.com/drone360/2015/03/27/drone-delivery-china
[November 2017].
European Commission (2017a). Automated Rail Cargo Consortium: Rail freight
automation research activities to boost levels of quality, efficiency and cost
effectiveness in all areas of rail freight operations [online]. Available at:
http://cordis.europa.eu/project/rcn/207495_en.html [December 2017].
European Commission (2017b). Smart automation of rail transport [online].
Available at: http://cordis.europa.eu/project/rcn/205952_en.html [December 2017].
European Rail Traffic Management System (ERTMS) (2014). From trucks to
trains: How ERTMS helps making rail freight more competitive [online]. Available at:
http://www.ertms.net/wp-
content/uploads/2014/09/ERTMS_Factsheet_1_From_truck_to_train.pdf [December
2017].
Financial Times. January 26, 2018. Meals on wheels: testing a pizza delivery
robot. Available at: https://www.ft.com/video/60ab7727-f9ca-465a-97f7-
13cfd770e5b7 [Accessed: April 2018]
Flämig, H. (2016). Autonomous vehicles and autonomous driving in freight
transport. In: Autonomous Driving. Berlin and Heidelberg: Springer, pp.365–385.
Franke, K.P. (2008). A technical approach to the Agile Port System. In: R.
Konings, H. Priemus and P. Nijkamp (eds) The Future of Intermodal Freight
Transport: Operations, Design and Policy. Cheltenham: Edward Elgar, pp. 135-151.
FURBOT (2014). FURBOT project brochure [online]. Available at:
http://www.furbot.eu/Pdf%20docs/Brochure%20FURBOT.pdf [January 2018].
Gattuso, D. & Pellicanò, D.S. (2014). Advanced methodological researches
concerning ITS in freight transport. Procedia – Social and Behavioral Sciences 111,
994–1003.
Gevaers, R., Van de Voorde, E., & Vanelslander, T. (2014). Cost modelling and
simulation of last-mile characteristics in an innovative B2C supply chain environment
with implications on urban areas and cities. Procedia-Social and Behavioral
Sciences, 125, 398-411.
Ginsburg, R. & Uygur, A.R. (2017). Changing technology in transportation:
Automated vehicles in freight, report for Illinois Department of Transportation
[online]. Available at: https://utc.uic.edu/wp-content/uploads/Automated-Vehicles-in-
Freight__Final-Report-08312017.pdf [Accessed: December 2017].
Guerra, E. (2015). Planning for cars that drive themselves: Metropolitan planning
organizations, regional transportation plans, and autonomous vehicles. Journal of
Planning Education and Research, doi.org/10.1177/0739456X15613591, 1–15.
New Technology and Automation in Freight Transport and Handling Systems
63
Haidari, L.A., Brown, S.T., Ferguson, M., Bancroft, E., Spiker, M., Wilcox, A.,
Ambikapathi, R., Sampath, V., Connor, D. & Lee, B.Y. (2016). The economic and
operational value of using drones to transport vaccines. Vaccine 34, 4062–4067.
Hars, A. (2015). Flotten selbstfahrender Elektrotaxis–Eine Szenarioanalyse. In
Entscheidungen beim Übergang in die Elektromobilität (pp. 615-632). Springer
Fachmedien Wiesbaden.
Hern, A. (2016). Amazon claims first successful Prime Air drone delivery.
Guardian, 14 December [online]. Available at:
https://www.theguardian.com/technology/2016/dec/14/amazon-claims-first-
successful-prime-air-drone-delivery [Accessed January 2018].
InnovaTrain (2017). Innovative technology for smart rail freight logistics [online].
Available at: https://www.unece.org/fileadmin/DAM/trans/doc/2015/wp24/ECE-
TRANS-WP24-2015-Pres01-2.pdf [December 2017].
Inrix (2018) Traffic congestion cost UK motorists over £37.7 billion in 2017, press
release [online]. Available at: http://inrix.com/press-releases/scorecard-2017-uk/
[February 2018].
Iwan, S., Kijewska, K. & Lemke, J. (2016). Analysis of parcel lockers’ efficiency
as the last mile delivery solution–the results of the research in Poland.
Transportation Research Procedia 12, 644–655.
Jackson, T. (2017). The flying drones that can scan packages night and day. BBC
News, 27 October [online]. Available at: http://www.bbc.co.uk/news/business-41737300
[January 2018].
Keeney, T. (2016). 3D printing: a disruptive innovation in its infancy. ARK Invest,
October [online]. Available at: http://research.ark-
invest.com/hubfs/1_Download_Files_ARK-Invest/White_Papers/3D_Printing_ARK-
Invest-WP.pdf?hsCtaTracking=06e1ba56-2964-40fb-a60b-
53a36b40c81a%7C12bc756c-dfd7-45b2-b363-9fb267e299d9 [January 2018].
Keeney, T. (2017). Autonomous electric trucks could disrupt rail and transform
logistics. ASK Invest, 10 October [online]. Available at: https://ark-
invest.com/research/autonomous-trucks [January 2018].
Korzhenevych, A., Dehnen, N., Bröcker, J., Holtkamp, M., Meier, H., Gibson, G.
& Cox, V. (2014). Update of the handbook on external costs of transport, European
Commission DG MOVE [online]. Available at:
https://ec.europa.eu/transport/sites/transport/files/handbook_on_external_costs_of_tr
ansport_2014_0.pdf [December 2017]
KPMG (2015). Connected and autonomous vehicles – The UK economic
opportunity [online]. Available at:
https://home.kpmg.com/content/dam/kpmg/pdf/2015/04/connected-and-autonomous-
vehicles.pdf [January 2018].
New Technology and Automation in Freight Transport and Handling Systems
64
Kunze, O. (2016). Replicators, ground drones and crowd logistics: A vision of
urban logistics in the year 2030. Transportation Research Procedia 19, 286–299.
Law Commission (2018).Automated Vehicles. Available at:
https://www.lawcom.gov.uk/project/automated-vehicles/
Lawrence, M., Roberts, C. & King, L. (2017). Managing automation:
Employment, inequality and ethics in the digital age. London: Institute for Public
Policy Research.
Lohr (2017). Lohr Railway System [online]. Available at: http://lohr.fr/lohr-railway-
system/ [November 2017].
McKinnon, A. (2016). The possible impact of 3D printing and drones on last-mile
logistics: An exploratory study. Built Environment 2, 617–629.
McKinsey Global Institute. (2013). Disruptive technologies: Advances that will
transform life, business, and the global economy. McKinsey & Company.
McKinsey Global Institute (2017). Harnessing automation for a future that works
[online]. Available at: https://www.mckinsey.com/featured-insights/digital-
disruption/harnessing-automation-for-a-future-that-works [Accessed January 2018].
McVeigh, K. (2018). ‘Uber for blood’: how Rwandan delivery robots are saving
lives. The Guardian, 2 January [Online]. Available at:
https://www.theguardian.com/global-development/2018/jan/02/rwanda-scheme-
saving-blood-drone [November 2018].
Ministry of Transport of Singapore (2017). Singapore to start truck platooning
trials, 9 January [online]. Available at:
http://www.portstrategy.com/news101/world/asia/singapore-to-start-truck-platooning-
trials [January 2018].
Moghadam, M.K. (2006). The impact of automation on the efficiency and cost
effectiveness of the quayside and container yard cranes and the selection decision
for the yard operating systems. PhD thesis, Liverpool John Moores University.
Molfino, R., Zoppi, M., Dinale, A. & Muscolo, G.G. (2014). A robotic vehicle for
freight delivery in urban areas. Paper presented at the 16th International Conference
on Harbor, Maritime & Multimodal Logistics Modelling and Simulation, September,
pp. 10–12.
Montoya-Torres, J.R., Muñoz-Villamizar, A. & Vega-Mejía, C.A. (2016). On the
impact of collaborative strategies for goods delivery in city logistics. Production
Planning & Control 27, 443–455.
Moody, C. (2016). End-to-end automation and the port of the future. Catapult
Transport Systems, 10 March [online]. Available at:
https://business.esa.int/sites/default/files/Topic%206%20-%20End-to-
End%20Automation%20and%20the%20Port%20of%20the%20Future.%20Presenter
%20-%20Chris%20Moody%2C%20TSC.pdf [January 2018].
New Technology and Automation in Freight Transport and Handling Systems
65
National Highway Traffic Safety Administration (2016). U.S. DOT advances
deployment of connected vehicle technology to prevent hundreds of thousands of
crashes, 13 December [online]. Available at: https://www.transportation.gov/briefing-
room/us-dot-advances-deployment-connected-vehicle-technology-prevent-hundreds-
thousands [November 2017].
Nelldal, B-L. (2017). Capacity for rail: SP2 requirements toward the freight
system of 2030–50, 21 September [online]. Available at:
http://capacity4rail.eu/IMG/pdf/08_wp2.1_requirementsforfreightsystem_nelldal._pptx
.pdf [November 2017].
Neuweiler, L. & Riedel, P.V. (2017). Autonomous driving in the logistics industry:
A multi-perspective view on self-driving trucks, changes in competitive advantages
and their implications [online]. Available at: http://www.diva-
portal.org/smash/get/diva2:1129922/FULLTEXT01.pdf [December 2017].
Oberhart, E. & Perry, E. (2017). The Missing Piece: Truck Parking Solutions for
Chicagoland. Paper presented at Poster Session, CPCS Transcom, Transport
Chicago, June.
Opensea.pro (2017). Autonomous ships and their impact [online]. Available at:
https://opensea.pro/blog/automated-ships [April 2018].
Priemus, H., Nijkamp, P., Konings, J. W. (2005). The future of automated freight
transport: Concepts, design and implementation. Cheltenham: Edward Elgar.
Rail Safety and Standards Board (2010). Research Brief – Driver advisory
information for energy management and regulation, T724, February [online].
Available at: https://www.rssb.co.uk/library/research-development-and-
innovation/research-brief-T724.pdf [December 2017].
Ranaiefar, F. (2012). Intelligent freight transportation systems, Institute of
Transportation Studies. Available at: https://docplayer.net/7337404-Intelligent-
freight-transportation-systems.html [October 2017]
Ricardo, TRL and TTR (2014). Heavy vehicle platoons on UK roads, 4 April
[online]. Available at:
https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/63736
1/truck-platooning-uk-feasibility-study.pdf [December 2017].
Ricci, S. (2017). Capacity for rail: Rail–road, rail–sea, marshalling yards:
Enhancements of interfaces, 21 September [online]. Available at:
http://capacity4rail.eu/IMG/pdf/10_wp23_railroadsea_ricci.pdf [December 2017].
Roland Berger. (2015). On the road toward the autonomous truck - Opportunities
for OEMs and suppliers. Roland Berger Strategy Consultants GmbH.
Roland Berger. (2016). Automated Trucks - The next big disruptor in the
automotive industry?. Roland Berger Strategy Consultants GmbH.
New Technology and Automation in Freight Transport and Handling Systems
66
Rolls-Royce (2016). Autonomous ships: The next step [online]. Available at:
http://www.rolls-royce.com/~/media/Files/R/Rolls-
Royce/documents/customers/marine/ship-intel/rr-ship-intel-aawa-8pg.pdf [April
2018].
Rosenzweig, J. & Bartl, M. (2015). A review and analysis of literature on
autonomous driving. The Making of Innovation, October [online]. Available at:
http://www.michaelbartl.com/co-creation/wp-content/uploads/Lit-Review-AD_MoI.pdf
[October 2017].
Society of Automotive Engineers International - SAE (2014) Taxonomy and
Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems
J3016_201401. Available at: https://www.sae.org/standards/content/j3016_201401/.
Society of Automotive Engineers International – SAE (2018) Taxonomy and
Definitions for Terms Related to Driving Automation Systems for On-Road Motor
Vehicles J3016_201806. Available at:
https://www.sae.org/standards/content/j3016_201806/
Schönknecht, R. (1983). Ships and shipping of tomorrow. Centreville, MD:
Cornell Maritime Press.
Scribner, M. (2016). Authorizing automated vehicle platooning: A Guide for state
legislators. Washington, DC: Competitive Enterprise Institute.
Shladover, S. (2012). Recent International Activity in Cooperative Vehicle–
Highway Automation Systems, U.S. Department of Transportation Federal Highway
Administration, December [online]. Available at:
https://www.fhwa.dot.gov/advancedresearch/pubs/12033/004.cfm [November 2017].
Shladover, S. (2017). Connected and automated vehicle policy development for
California. Policy Briefs 3, doi.10.7922.G25Q4T10.
Short, J. and Murray, D. (2016). Identifying autonomous vehicle technology
impacts on the trucking industry. Arlington, VA: American Transportation Research
Institute.
Široký, J. (2011). Automatic transhipment systems for container transport in
terminals. Perner’s Contacts 6, 145–154.
SNC-Lavalin (2017). Timetable Advisory System (TAS) [online]. Available at:
http://www.snclavalin.com/en/projects/timetable-advisory-system.aspx.
STARSHIP (2018). Press [online]. Available at: https://www.starship.xyz/news/.
Tavasszy, L.A. (2016). The value case for truck platooning, working paper, Delft
University of Technology. Doi 10.13140/RG.2.2.13325.54247.
Taniguchi, E., & Thompson, R. G. (Eds.). (2014). City logistics: Mapping the
future. CRC Press.
New Technology and Automation in Freight Transport and Handling Systems
67
Taylor, I. (2018). The true cost implications of failed deliveries. Post & Parcel, 1
February [online]. Available at: https://postandparcel.info/93399/news/e-
commerce/true-cost-implications-failed-deliveries/ [April 2018].
Tesla (2016). A tragic loss [online]. Available at:
https://www.tesla.com/en_GB/blog/tragic-loss140/RG.2.2.13325.54247 [December
2017].
Torrey IV, W.F. (2016). Cost of congestion to the trucking industry, American
Transportation Research Institute, May [online]. Available at: http://atri-
online.org/2016/04/19/cost-of-congestion-to-the-trucking-industry-2016-update/
[October 2017].
Trebilcock, B. (2011). The state of automation. Logistics Management, 24
February [online]. Available at:
https://www.logisticsmgmt.com/article/the_state_of_automation [October 2017].
TRL (2017). Government gives green light for first operational vehicle platooning
trial: TRL appointed to lead UK’s first HGV platooning trial, 25 August [online].
Available at: https://trl.co.uk/news/news/government-gives-green-light-first-
operational-vehicle-platooning-trial [January 2018].
Tsugawa, S., Jeschke, S., & Shladover, S. E. (2016). A review of truck
platooning projects for energy savings. IEEE Transactions on Intelligent Vehicles,
1(1), 68-77.
Viereckl, R., Ahlemann, D., Koster, A., & Jursch, S. (2015). Racing ahead with
autonomous cars and digital innovation. Auto Tech Review, 12(4), 18-23.
Wadud, Z. (2017). Fully automated vehicles: A cost of ownership analysis to
inform early adoption. Transportation Research Part A: Policy and Practice 101,
163–176.
Wainwright, O. (2015). Inside the London mega port you didn't know existed.
Guardian, 15 September [online]. Available at:
https://www.theguardian.com/artanddesign/architecture-design-
blog/2015/sep/15/london-gateway-megaport-you-didnt-know-existed-docks [January
2018].
Whitlock, C. (2014). When drones fall from the sky. Washington Post, 20 June
[online]. Available at:
http://www.washingtonpost.com/sf/investigative/2014/06/20/when-drones-fall-from-
the-sky/?utm_term=.f46983fad8a7 [January 2018].
Wiegmans, B.W., Hekkert, M. & Langstraat, M. (2007). Can innovations in rail
freight transhipment be successful? Transport Reviews 27, 103–122.
Wiśnicki, B., Chybowski, L. & Czarnecki, M. (2017). Analysis of the efficiency of
port container terminals with the use of the data envelopment analysis method of
relative productivity evaluation. Management Systems in Production Engineering 25,
9–15.
New Technology and Automation in Freight Transport and Handling Systems
68
Wood, Z. (2018,) Ocado uses £150m from shareholders for warehouse robots.
Guardian, 6 February [online]. Available at:
https://www.theguardian.com/business/2018/feb/06/ocado-uses-150m-from-
shareholders-for-warehouse-robots [February 2018].
Zářecký, S., Grúň, J. & Žilka, J. (2008). The newest trends in marshalling yards
automation. Transport Problems 3, 87–95
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