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Journal of Urban Mobility 4 (2023) 100059
2667-0917/© 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
The seventh transport revolution and the new challenges for
sustainable mobility
Ennio Cascetta
a
, Ilaria Henke
b
,
*
a
Universitas Mercatorum, Italy
b
University of Naples “Federico II”, Italy
ARTICLE INFO
Keywords:
Transport revolution
Sustainable Development
Mobilty of Future
ABSTRACT
Over the course of history there have been several and signicant changes in the methods and technologies used
to move people and things. Innovations typically follow the distributive and social needs of their time but in
some cases they drive or at least contribute to the social and economic evolution of human communities. The
paper is based on the hypothesis that transport system innovations occur with different speeds and have impacts
of different magnitude in different historical moments. Changes can thus be classied as revolutions or as
evolutions depending on weather they (contribute to) change societal, economic and/or territorial systems in a
relatively short time period or not. In this paper, analysing the history of humanity, six transport revolutions and
several evolutions following over time have been identied, extending and re-dening the ideas of Gilbert and
Pearl (2010). We suggest that transportation systems are undergoing a seventh revolutionary phase due to the
combined effects of three main drivers. These are innovations in energy sources and their transmission, de-
velopments of connected and autonomous vehicles for all transportation modes, and new smart mobility services.
As for past revolutions it’s impossible anticipate the extent of change it will bring about, both in the trans-
portation market and in society at large. According to the “law of unintended consequences” of previous revo-
lutions, the combined effects of the three drivers will likely further amplify the scope of possible changes. These
changes can have positive, neutral or negative effects on short to medium term for environmental, social and
economic sustainability of freights and passengers transportation. The paper starts with a synthetic description of
previous transport revolutions, as proposed by the Authors. The main elements of the seventh transport revo-
lution are then discussed together with some possible interactions among them. Finally the paper analyses some
opportunities and risks connected to the ongoing innovation with respect to environmental, social ed economic
sustainability. The perception of the current time as a revolutionary phase should change the approach of re-
searchers and practitioners in the wide eld of transportation system analysis with respect to the last evolu-
tionary decades. Future research, in addition to sector specic evolutions, should focus on the actual holistic
deployment of the seventh revolution trying to continuously update its combined effects and anticipate as much
as possible its trajectory in order to reduce undesirable ones while boasting desirable ones. Future transport
policies, especially in urban areas, will have to take into account the opportunities and risks deriving from the
ongoing transport revolution as well as the resulting level of uncertainty.
1. Introduction
Over the course of history there have been numerous and signicant
changes in the technologies and their use to move people and things.
Methods and technologies follow the distributive needs and economies
of their time but frequently also drive or at least contribute to the social
and economic evolution of human communities. In the pre-industrial
age, transport modes were functional to an agricultural economy
based on local production and consumption with limited long range
commerce. Progress in agriculture, industrialization and the consequent
increase in medium and long-distance trafc led to the need for faster
and cheaper transport modes.
The analysis proposed in this paper is based on the hypothesis that
innovations in transport systems take place at different speeds and have
impacts of different magnitude in different historical moments, similar
to what is assumed in the so-called punctuated equilibria theory of
* Corresponding author.
E-mail address: ilaria.henke@unina.it (I. Henke).
Contents lists available at ScienceDirect
Journal of Urban Mobility
journal homepage: www.elsevier.com/locate/urbmob
https://doi.org/10.1016/j.urbmob.2023.100059
Received 27 April 2023; Received in revised form 26 July 2023; Accepted 9 August 2023
Journal of Urban Mobility 4 (2023) 100059
2
evolution of the species (Gould & Eldredge, 1977) or the theory of sci-
entic revolutions (Kuhn, 1970). Innovations in transportation systems
include changes in traction power sources and vehicles, infrastructures,
and service organization, all which may bring about signicant changes
in the availability of transportation services in space and/ or time,
reliability, commercial speed and costs.
Changes can be classied as revolutions or evolutions. Generalizing
the denition proposed in the literature (Gilbert & Pearl, 2010), a
transport revolution can be dened as innovations in transportation
systems that produce and/or allow signicant societal changes, occur in
a limited time span with respect to the previous evolutionary period and
give rise to subsequent evolutions over an extended period of time.
Societal changes can be relative to dimension and structure of human
settlements, tradable goods and routes, production and distribution of
goods, personal and societal lifestyles, and the physical environment.
Technology innovations, however large, alone are not enough to be
considered revolutions. To qualify as a revolution, the adoption of new
technology should signicantly modify one or more fundamental
transport parameters (such as speed, cost, power, availability, etc.) in
such a way as to signicantly change the travel characteristics/ per-
formance and/or create new mobility needs/markets, in a relatively
short period of time.
In the literature, some authors see the possibility that ongoing in-
novations can signicantly impact society at large, (e.g. Cugurullo et al.,
2021, Gaio & Cugurullo, 2022) while others recognize the historical
nature of revolutionary innovations but see the ongoing one as driven
only by the change in energetic vectors (Gilbert & Pearl, 2010).
Taking into account the denitions of revolution and evolution in the
rst part of this paper, six transport revolutions that have occurred over
the course of human history, followed by evolutionary phases are pro-
posed (Section 2). The main contribution of this rst part is in the
identication of six revolutionary transport innovations occurred in
history as well as some elements common to the phases of “revolu-
tionary innovation” that can be useful for understanding what may
happen in the near future. In Section 3 the three main transport tech-
nological innovations, that could lead to a seventh revolution, will be
analysed. Decarbonization of traction energy, autonomous guidance of
vehicles and smart mobility services are currently being developed
together with some interactions among them. The system-level changes
that will follow from the seventh revolution can have positive, neutral or
negative effects on short to medium term for environmental, social and
economic sustainability of freights and passengers transportation. These
effects, however unpredictable in the long term, are discussed, as far as
we can anticipate, in Section 4 while Section 5 draws some conclusions
and propose some further research themes.
The original contribution of this paper is twofold. One is putting the
set of disruptive changes in a historical perspective, thus anticipating
some possible features of past revolutions, namely the unintended
consequences and super additivity, both increasing the uncertainty
about the effects of the current one. The other is recognizing that there
are at least three divers of the potential current revolution with their
multiple interactions dening what is going to be the possible mobility
of the future and that they are opportunities but also risks for what will
be sustainable development.
2. The rst six revolutions
Our species, the “homo sapiens”, evolved in Africa around 250,000
years ago and until the end of the last glaciation (about 10,000 years
ago), only moved by foot in groups of tens or hundreds of hunter-
gatherer individuals (Harari, 2014). For a long time, despite many
evolutions in the technologies of production of tools, weapons and ar-
tifacts, nothing changed in relation to their mobility. Homo sapiens
literally walked out of Africa and reached all major landmasses over
several millennia. In the last Eocene era the so-called agricultural rev-
olution took place, apparently in three different areas of the world, and
this set up a number of changes in the way men moved themselves and
their goods. These transport innovations can be classied as revolutions
or evolutions as dened in Section 1. We argue that in the course of
human history six transport revolutions took place as shown in Fig. 1
each revolution gave rise to subsequent evolutions that in some cases are
continuing to our days.
The rst revolution started around the year 8000 b.C. (Fig. 1) when
animals were domesticated and there is a transition from human traction
to animal traction. Persons and goods were moved using animals as the
source of traction power. Animal traction allowed the expansion of
cultivated land, the transport for produces and the rst urban agglom-
erations. The following evolutions are related to the expansion of the
number of domesticated animals suitable for traction.
The second revolution can be documented from 4000 b.C. when the
rst Egyptian papyrus sailing boats were represented. This is the rst
technology innovation that uses a traction force other than the
muscular, allowing longer and faster trips. Sail evolutions took place
over centuries improving their performances (e.g.their ability to go
upwind) until the second half of the 19th century when steamships take
over and there is the beginning of what will be the rapid decline of
commercial sailing. Sailing ships allowed travel over extended sea and
river distances, allowing the exchange of people and goods, as well as
the birth of Mediterranean civilizations. Sails are still evolving for
sporting and are facing a possible come-back to reduce CO
2
emissions
also for commercial use.
In 3500 b.C. in Mesopotamia, the Sumerians started the third revo-
lution by inventing the wheel thus substantially reducing the traction
effort needed to move the same weight on land. Since then, wheels
became increasingly differentiated and allowed the introduction of new
transport modes. The wagon was the rst wheel-based transport mode as
documented in Mesopotamia from the 4th mill. b.C. (Enciclopedia
Zanichelli, 1995). The combination of wagon and animals has evolved
over the millennia allowing land transportation over extended distances
starting the growth of cities and other larger and larger polities. Among
the evolutions relating to the wheel, it is worth mentioning the intro-
duction of two-wheeled vehicles with human traction force in 1791. The
“celerifero” was invented in France, evolving to the modern (muscular)
bicycle.
It is interesting to note that from the dawn of civilization until the
beginning of the nineteenth century, humans have used successive
evolutions of the same three technologies. For about 5000 years, while
cities, social rules, writing, mathematics, philosophy, physics, literature,
printing, guns and steal (Diamond & Ordunio, 2001) were introduced
and developed, people continued to move with animal-drawn wagons
and sailing boats and ships like their ancestors of the early Meso-
potamian civilizations. The fourth transport revolution started during
the rst decades of the19th century the steam engine was invented to
increase the productivity of mechanical looms at the dawn of the rst
industrial revolution, soon it was used to tow trolleys of material
extracted from coal mines. In 1804 the locomotive train suitable for
transporting goods and travellers, appeared (Burton, 2000). In 1807, the
rst steamboat invented by James Watt, sailed along the Hudson River
(Brown, 1991). The steam engine introduced chemical energy from coal
combustion, a new energy source w.r.t. muscular and wind energy. This
greatly increased the traction power and therefore the speed of land and
sea transport and, consequently changed the relations between cities,
ports and countries. The steam engine revolution brought about a series
of evolutions, for example the steam engine trains have gone from a 3
km/h speed (with the rst steam locomotive, invented by the English R.
Trevithick, in 1804 capable of carrying 70 people and 10 tons of iron) at
a speed of 175 km/h (German locomotive, in 1936) (Bergsteiner, 2005).
The development of steam railways and ships allowed the extension of
cities, the growth of industry, the beginning of transoceanic personal
travel and stronger land and sea commercial routes. The diffusion of
electricity has certainly revolutionized very important sectors of society
such as communications (telegraph, telephone, radio, etc.) but its
E. Cascetta and I. Henke
Journal of Urban Mobility 4 (2023) 100059
3
application to electric traction does not meet the denition of revolution
proposed in this paper. Functionally, electric trains did not provide
signicantly different performances from steam trains and the two
traction energy production technologies coexisted for many decades and
still coexist with internal combustion rail traction. Among the most
signicant evolutions of electric railway is the development of High
Speed Railways that started in Japan in the ‘60 s (Cascetta et al., 2020).
The fth revolution started in 1886 with the invention of internal
combustion engine (ICE). The internal combustion engine, which gen-
erates motion power by the chemical energy produced by the controlled
explosion of air and rened petrol blends, allowed the development of
innovative transport means due to the high energy content for unit
weight of rened oil. The availability of engines with high power and
fuels with a very high energy content allowed the diffusion of the car,
which after about 5000 years replaced the wagon and the carriage as a
transport means for few or individual travellers. The history of the car
begins in 1886, with the rst car patented by the mechanical engineer
Karl Benz. After a few years, in 1892, Rudolf Diesel patented the “Diesel”
engine, similar to the internal combustion engine but without spark
plugs. Thanks to their extraordinary power, these engines, were moun-
ted on large trucks and installed on heavy machinery. The internal
combustion engine revolution has also brought important evolutions in
public transport. The rst bus, the De Dion-Bouton, dates back to 1897,
while in Italy the rst bus was built by Fiat in 1906. The revolution of the
internal combustion engine allowed also the development of the
airplane and the consequent evolutions from the rst biplane of the
Wright brothers (1903) to modern jet airliners. Also in maritime trans-
port, the availability of diesel engines with great powers triggered a very
rapid evolution in the transport of people and goods.
In conclusion, society has been profoundly changed by the internal
combustion engine revolution, everything was profoundly and quickly
changed. It can be said that the last century was the oil century.
In the second half of the 20th century, the sixth revolution of freight
transport and logistics based on the container began in maritime
transport. A revolution with “low technological content”, which, by
reducing the cost of maritime freight transport by a factor of ten,
consequently changed the economic and geopolitical assets of the world,
giving way to the phenomenon we know as globalization (Donovan &
Bonney, 2006) where supply chains of several products could be
stretched over thousands of miles to save on labor and/or raw materials
and intermediate products.
The analysis of the six past transport revolutions shows that they
share two common elements (Cascetta et al., 2021a): the “law of unin-
tended consequences” and “super additivity” compared to previous
technologies. By “law of unintended consequences” we mean that the
innovation is generated by needs different from transport and/or leads
to forms of transport that are not foreseen in the early stages of adoption
of that innovation. For example, steam traction is based on technology,
the steam engine, conceived for operating weaving looms. Furthermore,
almost all revolutions use innovative combinations of new technologies
and technologies developed in previous revolutions. The phenomenon
by which the combined effect exceeds the “sum” of individual ones is
dened here as “super additivity”. For example, the car combines
pre-existing technology (the carriage) with an internal combustion en-
gine that replaces animal traction giving rise to completely different
performances and possibilities, such as mass production. Evolutions are
innovations in transportation systems improving the performances of
existing transportation services, even signicantly so, without produc-
ing signicant societal changes. In the case of evolutions, there is no
“law of unintended consequences”, changes arise explicitly to improve
technology or an organization of transport forms that already exist.
3. The three drivers of the possible seventh revolution and their
interactions
From the analysis of the six revolutions, it emerges that modern
trains, cars, ships and planes and the infrastructures and organization
they require are evolutions of technologies available seventy or a hun-
dred years ago. Nothing comparable, for example, to what happened in
the information, and telecommunication sectors (ICT) in the last few
decades (Sapolsky et al., 2018; Jorgenson & Vu, 2016; Groumpos, 2021;
Seel, 2022). In 1982, in the iconic lm “Blade Runner”, the future (2018)
was imagined with ying cars but with prehistoric computers and
without smartphones. The ying car is not the only example of a revo-
lution expected for at least seventy years that has not occurred. Today,
the mobility of the future is imagined as a system that allows the traveler
to choose the trip program from a menu of options with alternatives that
include electric autonomous vehicles, exclusive or shared with other
travellers, different pricing depending on the specic trip as well as on
different on board services and facilities, exchanging information with
each other and with the infrastructure to optimize the network.
Like any other complex socioeconomic system, the transport system
is internally complex, made up of many elements inuencing each other
both directly and indirectly, often nonlinearly, and with many feedback
cycles (e.g. number of travellers that use the infrastructure is inuenced
by the performances of physical elements, the performance of these el-
ements and the impacts of their use, are strictly connected to travel
demand and users’ behavior) (Cascetta, 2009). Apart from their internal
Fig. 1. The rst six revolution and their main evolutions (sources: elaboration starting Cascetta et al., 2021b).
E. Cascetta and I. Henke
Journal of Urban Mobility 4 (2023) 100059
4
complexity, transportation systems are closely interrelated with other
systems that are external to them, for example, environment, the quality
of life, and social cohesion (Cascetta, 2009). The changes of the trans-
port system, therefore, are inuenced by the evolutions that take place
in several different areas, such as technology, the economy, institutions,
behavior, culture, ecology and belief systems (Rotmans et al., 2001).
In the rst half of this century, some strands of technological and
organizational innovation are visible that will most likely lead to the
next transport revolution. Gilbert and Pearl (2010) anticipated the next
transport revolution, as essentially driven by decarbonization and the
change in energy production and use in transportation. We propose a
wider approach driven by three concurring areas of technologic in-
novations: i) decarbonization of transport, ii) self-driving vehicles con-
nected to each other and to infrastructures, iii) transformations in
mobility services (Cascetta et al., 2021b). These three drivers are already
underway, and they will continue to develop and interact in the next
decades
3.1. Decarbonization and new energetic vectors
Climate change is the biggest challenge for humanity. It is no longer
an exclusive problem of the scientic community, but rather a global
problem. In this context, European climate law will become a key
element of future EU regulation and the legislative process. The Green
European Deal as an EU climate and energy strategy has become an
important basis for climate legislation and is, among other things, the
result of the Paris agreements concluded during Conference Of the
Parties-COP21 (XXI Conference of the Parties of United Nations Framework
Convention on Climate Change). Since then other COP took place, up to
the last COP27 (XXVII Conference of the Parties of United Nations
Framework Convention on Climate Change) in Egypt progressively
dening targets and economic tools to reach them without penalizing
Countries with different development levels.
Sustainable development is dened by the united nations environ-
ment programme (UNEP) as the capacity to satisfy current needs
without compromising future generations (FAO, 2020), managing the
earth’s resources and minimizing climate change’s negative impacts
(Session S.W., 1987). This concept is so important that, in 2015, the
United Nations endorsed the 2030 Agenda for Sustainable Development
and 17 Sustainable Development Goals (SDGs), reafrming the World
Community commitment to Sustainable Development (Nations, U.
2015). Through this Agenda, 193 member states in a big action plan
with 169 targets seek to achieve the Millennium Development Goals
(Cartenì et al., 2020a). Among the 17 goals, the transport sector can
contribute signicantly to two aims: to reduce CO
2
emissions and in-
crease energy efciency.
On 14 July 2021, the European, Commission presented the ‘Fit for 55
′
package (EU Commission, 2021), its purpose is the implementation of
the European Union’s (EU) Green Deal in general and the climate
neutrality objective for 2050. As a rst step on the path to climate
neutrality, the 2030 GHG reduction objective has been raised to 55%
(with respect to 1990)—a binding commitment for the EU and all
Member States (Schlacke et al., 2022). On 14 July 2021, the European
Commission submitted a proposal: sectors covered by the Effort-sharing
Regulation (ESR) should achieve a collective reduction of 40% in their
emissions by 2030 compared to 2005 (European parliament (07/2022),
Revising the Effort-sharing Regulation for 2021–2030: “Fit for 55
″
package).
In this context is important to stress that in the European Union (EU),
the transport sector was responsible for 25% of greenhouse gas emis-
sions (GHG) in 2020. The emission of GHG within the agriculture sector
contributes 10.30%, industrial processes and use of products contributes
8.80%, and waste management contributes 3.27%. Moreover from 1990
to 2020 the transport sector experienced a slow decrease in its emissions
from fuel combustion in contrast to other major energy sectors (e.g.
Industry), therefore the incidence of transport has increased (from 20%
in 1990 to 25% in 2019).
Among the elds of action, most encouraged in the transport sector
to reduce its GHG footprint include electric vehicles and greater use of
energy-efcient appliances instead of conventional Internal Combustion
Engines (ICE) (Masson-Delmotte et al., 2018).
The main long-term strategies of European Member States agree on
the need to leverage a plurality of technologies to achieve the interna-
tional target of limiting global warming to below 1.5 ◦C compared to
preindustrial levels. In a recent study (The European House - Ambro-
setti, 2022), a total of 100 decarbonisation technologies have been
identifed that need to be analyzed and/ or promoted in order to optimize
investments following a principle of technological neutrality.
In Fig. 2 Production and usage of carbon neutral energy vectors.
In the transport elds, electric mobility seems to be one of the best
options to achieve both the sustainability goals and mobility needs at
least for passengers and Light duty vehicles (e.g. Gilbert & Pearl, 2010;
Prata et al., 2015; Cartenì et al., 2020a; Tsoi et al., 2022). Since the
2000s, opportunities and limitations of electric mobility are also widely
discussed among scientists and politicians (Carteni et al., 2020b).
The current main weaknesses of e-mobility concern the following: (i)
the high purchasing price (23–100% higher than a traditional vehicle)
(Table 1); (ii) the autonomy of the battery, which limits the maximum
distance travelled without recharging (despite the kms of a fully charged
electric vehicle being comparable to a traditional one, the limitation is
due to the recharging time not being immediate, which is different to the
other vehicles) (Plananska, 2020); (iii) the limited re-charging infra-
structure network (not enough to satisfy the demands of future potential
users) (Pavi´
c et al., 2020); (iv) the “no zero” carbon footprint related to
Life Cycle emission (for production, consumption and disposal) (Dang
et al., 2014); v) the electric vehicle has zero local impacts since it does
not pollute while it circulates, but the impacts deriving from the pro-
duction of electricity necessary to power the vehicle are anything but
zero (Carteni et al., 2020b) except in the case of production of renewable
energy for recharging the battery (e.g., wind energy, solar energy)
(Cartenì et al., 2020a, Fondazione Caracciolo, 2022).
Clearly “zero local emissions” is a not negligible advantage, espe-
cially referring to their usage in cities with high population density,
where the reduction of the local emissions could signicantly reduce
pollution and improve the quality of life (Cartenì et al., 2020a).
In the last decade, battery-powered electric vehicles (BEV) and plug-
in hybrid electric vehicles (PHEV), started to enter the market in sig-
nicant numbers (Fig. 3) and are expected to grow exponentially in the
next future due to reductions in capacity and costs of batteries. Indeed,
in 2021, around 16 million electric cars (BEV and PHEV) were on the
street in worldwide (Fig. 3). In 2021, the share of Electric Vehicles (EV)
for light commercial vehicles (LCV), Buses and Trucks is negligible
(Fig. 4). The search for sustainable mobility pushes car manufacturers to
offer electric vehicles with higher performance standards and at the
same time countries promote policies to encourage the use of this
transport mode. Moreover, during the last ve-ten years several car
manufacturers like Peugeot, and Mercedes started to produce just EV or
Hybrid cars, spreading electric production into the global car market. In
fact, some conventional ICE city cars like Smart ForFour or Smart For-
Two are no longer produced and will be replaced with their electric
model version. According to IEA (International Energy Agency) the
existing policies and measures, which have been legislated by govern-
ments around the world, will bring the global EV stock to expand
rapidly. Total EV will reach 200 million in 2030 (Fig. 4), with a signif-
icant increase of LCV-BEV since 2025.
Also the urban electric public transport eet can play an important
role in e-mobility being a solution to reduce Particulate Matter (PM)
pollution (Carteni, 2018¸ Di Pace et al., 2022). Indeed, most of the buses
circulating in European cities are powered by diesel (Acea, 2019) and
signicantly contribute to increasing polluting substances in the air such
as particulate matter and nitrogen oxides (ACI, 2020). Indeed, a bus that
travels one km damages human health like 20 cars that travel one km
E. Cascetta and I. Henke
Journal of Urban Mobility 4 (2023) 100059
5
(Carteni et al., 2020b). Electric bus systems are dened as environ-
mentally friendly, powerful energy-saving systems that are easily inte-
grated into high-quality sustainable urban transport (Kühne, 2010).
The Finnish government created a test platform used by several
electric bus manufacturers worldwide and this test platform became a
center for the assessment of manufactured prototypes (Erkkil¨
a et al.,
2013). Germany is working on silent and low-emission transport sys-
tems, which are also able to regenerate braking energy and store this
energy with ultra-capacitors with electric buses (Cartenì et al., 2020a).
In addition to electric mobility, hydrogen-powering fuel cells and
Fig. 2. Production and usage of carbon neutral energy vector (sources: The European House - Ambrosetti, 2022).
E. Cascetta and I. Henke
Journal of Urban Mobility 4 (2023) 100059
6
electric engines have emerged as a solution for climate change, air
pollution and energy security (K¨
orner et al., 2015). Although hydrogen
is the most abundant element on earth, it cannot be found by itself in
nature. Hydrogen can be produced from a variety of processes associated
with a wide range of emissions depending on the technology and energy
source used (European Commission, 2020).
Hydrogen generation technologies are increasingly being codied by
referring to a scheme based on different colors (Newborough & Cooley,
2020; Ivanenko, 2020; Noussan et al., 2020). The main colors that are
being considered are the following:
•gray (or brown/black) hydrogen, produced by fossil fuels (mostly
natural gas and coal), and causing the emission of carbon dioxide in
the process;
•blue hydrogen, through the combination of gray hydrogen and car-
bon capture and storage (CCS), to avoid most of the GHG emissions
of the process. While this approach seems to be less costly than
shifting towards green hydrogen, it is important to remember that
CCS implementation may involve technical barriers, in additions to
problems related to social acceptability. Blue hydrogen pathways
have currently technology readiness levels (TRL) between 7 (coal
gasication +CCS) and 8 (SMR +CCS) (Thomas, 2018);
•turquoise hydrogen, via the pyrolysis of a fossil fuel, where the by-
product is solid carbon;
•green hydrogen, when produced by electrolyzers supplied by
renewable electricity (and in some cases through other pathways
based on bioenergy, such as biomethane reforming or solid biomass
gasication). The green hydrogen pathway is dened as the combi-
nation of power generation from renewable sources and water
electrolysis. By supplying electricity and pure water to an electro-
lyzer, output ows of hydrogen and oxygen are produced;
•yellow (or purple) hydrogen, when produced by electrolyzes sup-
plied by electricity from nuclear power plants.
Blue hydrogen pathways have the advantage of building on existing
industrial experience from gray hydrogen, and in some cases retrotting
of existing plants could be performed by adding CCS systems. However,
specic conditions need to be met to ensure effective and durable stor-
age of CO
2
.
A widespread and effective development of green hydrogen requires
a notable amount of renewable electricity, which may be a problem in
the short term, since RES (renewable energy sources) are already needed
to decarbonize existing electricity demand. For this reason, blue
hydrogen can represent a useful option in the short and medium term, by
helping in paving the way for green hydrogen at a later stage (Dickel,
2020).
Germany is among the most active nations on this issue, in fact,
hydrogen transportation and infrastructure in Germany are currently
Table 1
Comparison between the average purchase price of private electric/traditional
(diesel/ gasoline) car (source: elaborations on www.quattroruore.it, last access
February 2023).
Tipology Segment Model Price
(
€
)
% var. (electric-
traditional)
Sport Utility
Vehicle
F Jaguar I-Pace
(Electric)
83,690 23%
JAGUAR F-Pace 68,100
Crossover Utility
Vehicle
B Peugeot e-2008
(Electric)
40,980 65%
Peugeot 2008 24,900
Sedan (Berlina) B Peugeot e-208 36,180 103%
Peugeot 208 17,820
Fig. 3. Global electric passenger car stocks, 2010–2021 (source: Global EV Outlook 2022, International Energy agency, 2022).
Fig. 4. Global EV stocks by mode, 2021–2030 (source: Global EV Outlook
2022, International Energy agency, 2022).
E. Cascetta and I. Henke
Journal of Urban Mobility 4 (2023) 100059
7
developing (Herwartz et al., 2021). As of June 2020, there have been 89
hydrogen refueling stations (HRS) in operation mode for road vehicles in
Germany. The government’s aim is to establish 400 HRSs by 2023.
Additionally, about 54% of the German rail network is not electried.
On those non- or partly electried tracks, diesel-fueled trains (diesel
multiple units e DMU) are being operated. Those could potentially be
replaced with hydrogen-powered trains (fuelcell electric multiple units e
FCEMU) (Pagenkopf et al., 2020). In 2018 the world’s rst two FCEMU
were put in scheduled passenger service in Germany.
Regarding the freight and logistics sector, in urban areas the policies
most common regarding the adoption of electric freight vehicles (EFVs)
instead of conventional Diesel-fueled trucks in urban pickup/delivery
operations (Fiori & Marzano, 2018). On the other hand, BEV heavy
freight vehicles (HFV) are currently seen as a noncompetitive solution
given the battery weight needed for long-haul trips. Hydrogen fuel cells
and bio-methane are considered more competitive solutions due to their
higher energy density. Hydrogen and bio-methane have energy densities
of about 33 kWh/kg and 14 kWh/kg, respectively; while electric bat-
teries reach 0.4 kWh/kg. BEV HFVs for long-haul trips would weigh
much more than fuel cells or bio-methane HFVs, losing the advantage of
exploiting a more efcient powertrain. Indeed, the greater the weight,
the more energy must be expended to move the truck. Similar consid-
erations are being made for bio-diesel (HVO) powered vehicles. Recently
bio-fuels are emerging as candidates to power last generation diesel
engines (Euro 6) with reduced or null CO
2
emissions on the WTW cycle
(e.g. Lilja et al., 2020; Mantziaris et al., 2020). In fact, the biomasses
used as a source for the production of biofuels subtract CO
2
from the
environment during their life cycle. To produce, transport, and
distribute a biofuel energy unit (WTT), CO
2
is released into the atmo-
sphere. A "bio credit" equivalent to the CO
2eq
emission given by the
combustion of the energy unit produced and corresponding to the
quantity of CO
2
removed from the environment by the biomass must be
subtracted from this contribution. Given that the biomasses used for
second-generation biofuels exploit agricultural waste or ad hoc crops,
the "recovered" CO
2
, counted in the "bio credit", would be "lost" if not
used to produce biofuels. In fact, the natural decomposition would still
cause the emission of CO
2
captured by the biomass during the life cycle.
The value of the "bio credit" depends on the biomass used as an energy
source(Prussi et al., 2020). The TTW contribution depends on the
vehicle analyzed and is always considered positive. The WTT contri-
bution will be conditioned by the "bio-credit". In the Fig. 5, a simplied
chart for the case of bioethanol, where the emission contributions are
shown in blue; in green are reported the "bio-credit" accumulated by the
specic type of biomass used. A positive sign is attributed to
climate-altering emissions, a negative sign to "bio credit" since it is
considered as a quantity of CO
2
subtracted from the environment.
The HVOs (Hydrotreated Vegetable Oils) are advanced biofuels
produced by rening vegetable oils or animal fats through a hydroge-
nation process. HVOs are considered less polluting than other biofuels
such as biodiesel, as the hydrogenation process removes almost
completely nitrogen oxides (NOx) and volatile organic compounds
(VOC), which are air pollutants that can have negative impacts on
human health and the environment. HVOs can be used as direct sub-
stitutes for fossil diesel without the need to modify the engine of vehi-
cles, and therefore represent a more immediate solution for reducing
CO
2
emissions in the transport sector (Lilja et al., 2020). A study pub-
lished in Nature Energy in 2021 (Blanco et al., 2021) highlighted that
the use of advanced biofuels produced from non-food biomass could
contribute to more efciently reducing greenhouse gas emissions in the
transportation sector compared to complete electrication of the vehicle
eet. Another research (Larsen et al., 2020) concluded that, in some
parts of the United States, advanced biofuels may be more effective in
addressing greenhouse gas emissions in the transportation sector than a
full conversion to electric vehicles. However, it is important to note that
biofuels also have some environmental limitations and issues, such as
the need for land and water resources for biomass cultivation and
greenhouse gas emissions associated with biofuel production and
transportation (e.g. Gopalakrishnan et al., 2019; Singh et al., 2021; Li
et al., 2022).
The electrication of maritime transport is progressing but is
currently limited to ferries and other short-haul ships. The low energy
density provided by the electric batteries excludes the complete elec-
trication of ships. At the moment the more promising solutions for
regular ships are uncertain and connected to Hydrogen, but due to cost
and problems related to storage and energy demand for compression/
liquefaction, it is not yet clear whether it will become the main energy
carrier in the sea transportation sector (Zhou, 2005; Machaj et al.,
2022). However, second-generation biofuels might represent an inter-
esting solution since they offer similar energy densities of traditional
fuels and would be able to supply ICE engines, requesting no redesign of
powertrains (Napolitano et al., 2022). In alternative fuel experimented
for regular ships are based on Ammonia, which is an excellent hydrogen
carrier (Dimitriou & Javaid, 2020) and is favored by the maritime sector
(e.g. Al-Breiki & Bicer, 2020; Baldi et al., 2019, Laval et al., 2020).
Ammonia is one of the most widely produced inorganic chemicals, in
2019, approximately 240 million tons of ammonia were produced and it
is predicted that in 2030 production will increase to approximately 300
million tons (Laval et al., 2020; Machaj et al., 2022). Most of it is pro-
duced in four countries: China, India, Russia, and the United States, but
is traded worldwide (Machaj et al., 2022).
In the last decade, there are different port operating policies with the
aim to reduce emissions from shipping during the berthing phase;
however, their efcacy varies for different ports. Cold ironing (CI) or
alternative maritime power (AMP) denes the procedure of providing
Fig. 5. CO
2
equivalent- WTW calculations- simplied chart (sources: Prussi et al., 2020).
E. Cascetta and I. Henke
Journal of Urban Mobility 4 (2023) 100059
8
electrical power to a ship at berth to meet the ship’s energy demands
while the ship’s main and auxiliary engines are switched off. This
technological solution can eliminate local emissions as the ships’ funnels
do not release pollutants in port. Global GHG reductions will depend on
the origin of the energy providing the shore power (with increased
emissions locally near the power supplier) (Zis, 2019).
3.2. Autonomous and connected vehicles
All transport vehicles have been driven by humans since the rst
revolution. This is going not to be the case in a few decades.
Road mobility at the moment is almost entirely controlled by drivers
perceptions, reactions and errors. Road vehicles are undergoing a
transformation in their control and driving systems through a combi-
nation of advanced sensor technology; on-board and remote processing
capabilities; GPS and telecommunications (5 G) systems (e.g. Connected
and Autonomous vehicles). The main innovations are expected from the
automotive world up to the Autonomous Car (or robot car), a self-
driving car that uses a combination of sensors, cameras, radars and
articial intelligence (AI). This technology allows moving between
different destinations without the need for human intervention, even on
roads that have not been pre-adapted for the purpose. The Society of
Automotive Engineers (SAE, 2014) has classied automated vehicles
into six levels of driving automation, ranging from Level 0 (no auto-
mation) to Level 5 (full automation) Fig. 6. In the literature road
autonomous vehicles are referred to as AV (autonomous vehicle), CAV
(connected AV) and CCAV (cooperative connected autonomous
vehicles).
To date, most current vehicles have acquired Level 1 or Level 2 since
these vehicles are equipped with ADAS technologies such as cruise
control, hazard warning, and automated parallel parking. The automo-
tive industry requires several years of development, testing, and
approval to completely reach Level 5 of self-driving vehicles. Currently,
carmakers are competing to acquire fully autonomous features for their
vehicle design. Some Companies already sell Level 3 cars such as Mer-
cedes and Honda allowing automated driving in some Driving Trafc
Domains, typically motorways. Other have been implementing Level 4
pilot projects to test AVs under certain circumstances such as specic
road types, areas, and weather (Ahmed et al., 2022). For example,
Waymo (Google’s company for the development of self-driving, one of
the pioneering companies in the development of the level 5 driverless
car, such as to have covered, to date, over 32 million km and recently
collected an external investment of 2.25 billion dollars), robotaxi
designed for the transport of people compliant with level 5 of
self-driving, is available in the city of Phoenix, Arizona (see Section 3.4).
However, today, it would seem Level 4 does not appear to be designed
for use in the private car market (but mostly for taxis), but some level 4
features are useful for private cars, such as Intelligent Park Pilot. Intel-
ligent Park Pilot (level 4) (Mercedes-Benz is developing this technology)
allows a fully automated parking activity, the driver has the possibility
to leave the car, which will park autonomously. The Volvo company is
investing in the level of automation by focusing on Ride Pilot technology
based on the high redundancy of cameras and sensors (including the
LiDAR of Luminar) and on the constant updating of the software in OTA
(over-the-air) mode. The car can park (with autonomous driving) even
in parking lots distant from the place where the driver has abandoned
Fig. 6. The six levels of driving automation (sources: Society of Automotive Engineers).
E. Cascetta and I. Henke
Journal of Urban Mobility 4 (2023) 100059
9
the vehicle (Stein, 2020). This will lead to a change in urban areas and in
particular in city centers, in fact, if in the peripheral areas will be built
special parking lots for autonomous vehicles, the central areas will be
freed from parking areas and those areas can be used for other purposes.
Cruise (of General Motors) also announced its plans: 1 million autono-
mous cars by 2030 used for public and private taxis services. Cugurullo
et al. (2021) assumed that AV
S
will be the dominant form of urban
transport by 2040, especially in cities like San Francisco, London,
Pittsburgh, Gothenburg, and Singapore, where today this new technol-
ogy is tested in real-life environments.
The automation level SAE 4 seems to have a short term higher
adoption potentiality for local public transport in urban contexts, as
shown by some pilot experiences. Some prototype versions of Level 4
and 5 vehicles are starting to be applied for airport shuttle services and
minibus lines, such as in the UK (Lo, 2012), France (E. NAVYA, 2016)
and Germany (EasyMile, 2021), where vehicles run at very low speeds
ranging from 15 to 20 km/h (e.g. Hagenzieker et al., 2020) in pedestrian
areas and/or along enclosed and restricted routes limiting their move-
ments and interactions with other vehicles and people. However, such
technological limitations are widely expected to end in the foreseeable
future, allowing automated vehicles (AVs) unlimited movement on
roads jointly with both traditional vehicles and pedestrians (Hulse et al.,
2018). Recently trafc ow model able to support the implementation of
trafc management strategies in the presence of human-driven and
connected vehicles are proposed (e.g. Storani et al., 2021; Storani et al.,
2022a; 2022b)
The spread of self-driving vehicles in the market is linked to two
main problems: 1) technological, 2) regulatory. The main technological
problems concern safety issues related to driving automation (Levels 4
and 5), especially in urban areas where there are both traditional ve-
hicles and pedestrians (e.g. Martinho et al., 2021, Nyholm & Smids,
2016, Ethik-Kommission, 2017). Ordinary trafc situations are the
day-to-day interactions with pedestrians, cyclists, animals, that require
some exibility, such as crossroads, highway entrances, or crosswalks
with limited visibility. These interactions are challenging for AVs not
only because these systems lack human intuition and exibility but also
because of the large scale eet programming that is needed (Martinho
et al., 2021). If on one hand, self-driving car technology reduc-
es/eliminated accidents for human error (Ethik-Kommission, 2017),
furthmore, in dangerous situations, algorithms are able to decide within
a fraction of a second, whereas human drivers become panicked and act
on their instinct (Nyholm & Smids, 2016), the ethical issues are an issue
to be resolved (Ethik-Kommission, 2017).
From a regulatory point of view, the European Union has recently
approved UN Regulation No. 157, (2022) which provides uniform
guidance to EU member countries on the safety certication of auton-
omous vehicles. The Regulation sets out clear performance-based re-
quirements that must be complied with by car manufacturers before
equipped vehicles can be sold. The new functionalities must also comply
with the strict cybersecurity and software update requirements outlined
in the relevant UN Regulations. The directive holds motor vehicle
manufacturers responsible for the safety of their products. This means
that if a motor vehicle is involved in an accident and does not meet the
safety requirements set forth by the directive, the manufacturer may be
held responsible for the accident and may be required to pay for any
resulting damages. In the near future, important legal decisions will
need to be made regarding criminal responsibility, for example, if an
autonomous vehicle (level 5, without human intervention) collides and
causes the death of a nearby pedestrian (Imai, 2019). Additionally, UN
Directive No. 157 includes among its main innovations the allowance of
level 3 vehicle driving on motorways, where pedestrians and cyclists are
prohibited and the trafc moving in opposite directions is separated by
physical barriers. Level 3 driving is allowed at speeds of up to 130 km/h,
and automated lane changes are also permitted.
Furthermore, the spread of AV will be determined also by social at-
titudes, and how individuals feel about autonomous vehicles.The
research carried out by Cugurullo et al. (2021) has empirically shown
that, in Dublin, people are generally concerned with the safety of
autonomous vehicles and, yet, inclined to use them once available.
As mentioned, the innovation of self- and connected driving on the
road does not only concern vehicles, but also the infrastructure and
interactions among vehicles. Allowing communication and connection
with the vehicles that travel is one of the goals of smart roads (Decreto
number 90, 2018 on Smart Road). The smart road is a synthesis of
automatic data detection technologies such as cameras, radars, sensors
in the pavement, of fast and bidirectional communication technologies
(Vehicle to infrastructure V2I vehicle to infrastructure and vehicles V2X)
between sensors, vehicles and control center, platforms for analysis,
trafc forecasting and management of control interventions in ordinary
and emergency ow conditions. There are several examples of smart
roads in Europe, among these the pilot project C-ROADS, in Austria, can
be mentioned that started in February 2016 and ended becoming
operational in 2019 which affects the motorways connecting Vienna and
Salzburg, the Brenner corridor and the surroundings of Graz. The project
consists of equipping 300 km of motorways with C-ITS (Connected
Intelligent Transportation Systems). It resorts to the support of the ITS-
G5 mobile network to provide C-ITS services such as accident notica-
tions, roadworks alerts and in-vehicle weather reporting. Mayor smart
road projects are currently under way in Italy (ASPI, CAV, ANAS).
Self-driving is a technology already widely used for other modes of
transport, such as driverless rail systems. The world’s rst automated
driverless Railway opened in Kobe (Port Island Line) in 1981, Japan. The
second in the world (and the rst such driverless system in Europe) was
the Lille Metro in northern France in 1983. In this system, trains can
operate automatically all times, including door closing, obstacle detec-
tion and emergency situations. All vehicles are continuously managed
from a centralised control center and on-board staff may be provided for
other purposes, for example customer service, but are not required for
safe operation.
In october 2021, a driverless train makes its rst trips on the urban
network in Hamburg. The train was developed by Siemens and Deutsche
Bahn and is part of ‘Digital Rail Germany’. Hitachi Rail and Rio Tinto
have build a self-driving freight train in Pilbara region in western
Australia. This system, operating by the end of 2018, is the world’s rst
and only fully automated rail system for long-distance heavy freight
transport. It enables 220 trains, monitored remotely from an operations
center in Perth, to safely and efciently across more than 1866 km of
track.
A fully autonomous freight train will be ready on Finnish tracks by
the end of 2023. Led by Finnish technology company Proxion, the
freight train will have Automatic Train Operation (ATO) over the Eu-
ropean system ETCS (European Train Control System).
About freight digital transformation there is the possibility of con-
necting more trucks to form a train (Truck platooning).A truck platoon
includes a lead truck (leader) and one or more trailing trucks (followers)
and is dened by the European Automobile Manufacturing Association
(ACEA) as “the linking of two or more trucks in convoy, using connec-
tivity technology and automated driving support systems. These vehi-
cles automatically maintain a set, close distance between each other
when they are connected for certain parts of a journey, for instance on
motorways.
There are different levels of automation here as well. The charac-
teristics of level 1 are: leaders and followers with driver on board,
possibility of opportunistic platooning (on the y) or by appointment
and only V2V communications are required. The characteristics of level
2 are: leader with driver on board, followers with autonomous driving or
with driver at rest (change of rules), need for truck platooning stations
on portions of the network (highways) and need for V2V and V2I
communications (smart road). Finally, the characteristics of level 3 are:
leaders and followers with autonomous driving, the need for truck
platooning stations on portions of the network (highways) and the need
for V2V and V2I communications (smart road). Studies (e.g. Marzano
E. Cascetta and I. Henke
Journal of Urban Mobility 4 (2023) 100059
10
et al. 2022) have shown that truck platooning has a signicant potential
market share in medium to long distance journeys, increasing with the
level of automation, and cost reduction.
Truck platooning is not the only step toward autonomous trucks.
ADAS functionalities such as adaptive cruise control (ACC), automatic
emergency braking (AEB) and lane keeping assist (LKA) are accelerating
for commercial vehicles (). In the near future, attention will be directed
towards accelerating Level 4 in order to offer autonomous capabilities in
last-mile delivery. Several car manufactures (e.g. Tesla,Volkswagen,
Volvo) are investing in new technology with the aims to roll-out L4
autonomous capabilities in the near future. Volvo Autonomous Solu-
tions have constituted a new business area as of January 1, 2020, Tesla
plans to market with Autopilot capabilities and offer at least 500 miles of
range (Future Bridge, ADAS in commercial vehicles, Q2’20 Pulse). As
with AVs, for autonomous commercial vehicles technological challenges
and regulatory barriers still exist. Adoption of standard legal and in-
surance framework will help to accelerate the testing and faster
commercialization of autonomous trucks. There are also logistics
groups, such as FedEx, which is ready to launch an autonomous Heavy
Goods Vehicles service in 2023 in the US.
3.3. Smart mobility services
The development towards smart mobility is being led by the tech-
nology sector, use of new ICT, IoT and RFiD technologies in the eld of
transport services. It has and will change users’ mobility in different
markets. In the last twenty years, the mass adoption of the world wide
web, and the diffusion of the smartphone have transformed many as-
pects of everyday life, modifying, in less than a generation, the ways
people communicate, organize patterns of work, shopping, and social-
izing and transforming the idea of mobility (Docherty et al., 2018).
The use of these telecommunication technologies in mobility services
involves (Curtis & Lehner, 2019):
•Improving access to information (Kim & Yoon, 2016; Pisano et al.,
2015)
•Facilitating intermediation between providers and users (B´
alint &
Tr´
ocs´
anyi, 2016; Schor et al., 2016)
•Facilitating payments (Cartwright, 2016)
•Increasing trip convenience (Butenko, 2016)
New technology based mobility services have several declinations
and also different names for similar instances. In this paper we dene
“Smart mobility” as the set of new mobility-related services made
possible by ICT technologies. This denition is general enough to
include very different services that have been proposed over the last few
years as well as some that will be proposed in the next. The services
proposed so far can be grouped in four classes and their combinations:
•Info mobility
•Sharing mobility
•Vehicle Sharing
•Ride Sharing
•Smart pricing
•Mobility as a Service (MaaS)
Info-mobility refers to systems that allow travellers and companies to
access trip related information before the trip (to allow for decision-
making on the options to choose based on several factors including
prices, speed and timeliness) and during the trip (to be informed in real-
time on the variation of the trip characteristic) (Peprah et al., 2019).
Different platforms suggest routes for road networks based on real time
travel times estimated through all service users,Google Maps and WAZe
are among the most used. For the time being, route information and
suggestions are based on travel times and possibly tolls, but is possible to
anticipate more sophisticated predictions/ suggestions based on other
users’ specied attributes such as reliability, type of roads, number of
stops, driving stress and so for. With Google map it is possible to choose
“eco-friendly routing” (available from October 2021 in U.S. and from
October 2022 in Europe). Several platforms provide real time informa-
tion in Public Urban Transportation services, regional and intercity rail
services, ights etc.
The sharing mobility is linked the terms “sharing economy”,
“collaborative consumption”, “peer to peer economy”, all terms to
describe the phenomenon as peer to peer sharing of access to underu-
tilized goods and services, which prioritizes utilization and accessibility
over ownership (e.g. Cheng, 2016; Schor & Fitzmaurice, 2015). The
sharing economy is largely promoted in academic literature as offering
access over ownership (Martin, 2016; Light & Miskelly, 2015), by
leveraging the idling capacity of goods and services (e.g. Harmaala,
2015; Heinrichs et al.,;, 2017), in order to reduce our overall con-
sumption and subsequent resource use (e.g. Ala-Mantila et al., 2016; de
Leeuw & G¨
ossling, 2016) Different economic model could be used in
sharing economy, the most popular for sharing mobility are:
•Peer-to-peer (or consumer-to-consumer) model implies that the eet
of vehicles (cars, bikes, moto) is owned by a community. The
marketplace then matches cars that are available by the owners with
the prospective drivers willing to rent them. In this model, private
individuals rent their vehicles in exchange for nancial compensa-
tion. Companies such as Turo (formerly RelayRides), Getaround, and
JustShareIt offer examples of peer-to-peer car sharing.
•Business-to-consumer model means that a company owns a eet of
vehicles (cars, bikes, moto) and facilitates the sharing among mem-
bers. Multiple drivers have access to the same vehicle, owned by
companies that rent by the hour or by the day (e.g. car-sharing, bike
sharing) Auto manufacturers (e.g., BMW, Peugeot, Daimler), rental
brands (e.g., Hertz, WeCar), and car-sharing brands (e.g., Zipcar,
StattAuto, GoGet) offer examples
Sharing mobility has grown rapidly in recent years, probably because
of great recession of 2007–2008 (e.g. Aptekar, 2016; Cohen et al., 2016;
Morgan & Kuch, 2015; Posen, 2015), health emergency for COVID-19
(e.g. Lukasiewicz et al., 2022), increased environmental awareness (e.
g. Ala-Mantila, 2016; Butenko, 2016, Cohen & Kietzmann, 2014; Mat-
zler et al., 2014) and proliferation of ICT applications (e.g. Tussyadiah,
2016; Hamari et al., 2016). According to the analysis conducted by
Statista (E. Statista, 2022b) the revenue in the Shared Mobility segment
is projected to reach US$1.18tn in 2022. Revenue is expected to show an
annual growth rate (CAGR 2022–2026) of 10.38%, resulting in a pro-
jected market volume of US$1.74tn by 2026 (Fig. 7). In global com-
parison, most revenue will be generated in China (US$312.50bn in
2022).
The market’s largest segment is Shared Vehicles with a projected
market volume of US$0.80tn in 2022. The number of business-to-
consumer (B2C) car sharing users worldwide has increased, by 2025
carsharing users will reach 36 million, maintaining an annual growth
rate of 16.4% (Fig. 8). In Europe, the number of vehicles in car sharing
programs went from 7.500 in 2006 to around 61.000 in 2018 with an
increase of 710% and the growth in the number of car sharing users in
Europe from 2011 to 2020 is equally signicant, from about 700.000 to
about 15 million. In Italy, car sharing services began to take hold at the
beginning of the 2000s thanks to some “local” experiments; but the real
boom was recorded in 2013, with an increase of 330% compared to
2012 in the number of users, which went from 30.000 in 2012 to
130.000 in 2013. In 2019, car sharing in Italy counted over 2,2 million
users and about 8.300 vehicles and 12 million rentals of which 3%
employed in station-based car sharing and the remaining 97% in free
oating, testifying to the signicant importance that the latter type has
been hiring in our country in recent years.
Another case of sharing economy is ride sharing, people sharing the
same vehicle for travel (Novikova, 2017). Various dynamic ride-share
E. Cascetta and I. Henke
Journal of Urban Mobility 4 (2023) 100059
11
Fig. 7. REVENUE in shared mobility in worldwide. (sources: E. Statista, 2022b).
Fig. 8. Number of carsharing users worldwide (Source: Frost & Sullivan, Future of Car Sharing Market to 2025).
Fig. 9. The sharing in freight sector: “Flock Freight’s shipping solution for the aim of carbon neutral” (sources:https://www.ockfreight.com/blog/what-is
-ockdirect/).
E. Cascetta and I. Henke
Journal of Urban Mobility 4 (2023) 100059
12
systems aim to bring together travellers with similar schedules and
itineraries on short notice, this is the case of Car-pooling(or ride hailing
– vehicle owners allowing other passengers to ride in the same vehicle to
and from the same or similar destinations). Other scheme of ride sharing
is on-demand ride service, the driver owns the vehicle and picks up
people, offering a exible door-to-door service, with Uber being the
most prominent example of a platform enabling peer-to-peer trans-
actions (Cohen & Kietzmann, 2014).
Also, the freight sector is developing the concept of sharing, even
though at a slower pace. For example, Flock Freight is a shared truckload
service (Fig. 9), which enables several businesses to share trailer space in
one multi-stop full truckload. This is a hubless shipping mode that uses
advanced algorithms to pool midsize freight (between four to 22 pallets)
from multiple shippers when it’s moving in the same direction with the
aim of lower the shipping carbon footprint.
New technologies can be useful for designing congestion pricing
(crediting) schemes more equitable and acceptable for users. The road-
pricing is a well-established policy option with the aim to reduce
congestion in central areas and to reduce environmental impacts (Cas-
cetta et al., 2017). It could be possible to group the main pricing schemes
into:
•toll pricing, for which the user pays for crossing a road infrastructure;
•cordon pricing, for which the user pays for crossing a cordon (e.g. for
entering in a city);
•area pricing, for which the user pays for entering into a specic
restricted area (e.g. for moving within an Historical area in the city
center).
The development of the Intelligent Transportation System (ITS)
technologies allow and will allow in the next years to extend and apply
these pricing schemes (both for passenger and for freight) in several
different and most rational (sustainable) ways, connecting the price both
to the individual characteristics of the trip and to the type of vehicle
used, as for example (Cascetta et al., 2017; Cascetta & Montanino,
2022):
•distance-based, where the price is dened in function of the distance
travelled;
•time-based, function of the hour of the day/season of the trip (e.g.
peak vs. off-peak hours; summer vs. winter months);
•congestion-based, function of the congestion level of the path/road
used;
•vehicle-based, function of the vehicle typology used for the trip (e.g.
electric vehicle vs. traditional vehicle; old vehicle vs. new vehicle,
light goods vehicle vs. heavy goods vehicle) and/or to the loading
factor (e.g. 1 user/car vs. 2–5 users/car).
The set of all smart mobility services can be collected in a MaaS
system (Mobility As A Services). MaaS is a new concept of mobility that
provides for the integration of multiple public and private transport
services, generally belonging to multiple modes of transport and oper-
ated by a variety of operators, accessible to the end user through a single
digital channel (e.g. Jittrapirom et al., 2017; Kamargianni et al., 2018).
The MaaS concept promotes a digital future, simple, accessible and
multimodal, which will allow users to move more easily and plan their
trips directly from a mobile app, which will allow them to perform all
the operations: from choosing the best route (and pedestrian path) to
checking the availability of vehicles, booking them, paying for the entire
route, consulting their movements, for a new and complete "mobility
experience". Depending on the service offered (e.g. buying a ticket with
the app, allowing the integrated ticket relating to the entire trip) and
integration with other transport modes (planning integration), different
MaaS can be dened and different impact can be observed (e.g. Ranta-
sila, 2015; Karlsson et al., 2016; Jittrapirom et al., 2017; Sochor et al.,
2018; Storme et al., 2021).
3.4. Some possible interactions among innovation streams
The three drivers of the seventh revolution are being developed by
separate industry segments but they have a very high potential for in-
teractions dening what is going to be the possible mobility of the
future. For example, the decarbonisation of transport can be facilitated
by the development of sharing mobility. Sharing mobility, the use of the
electric vehicle combined with the sharing mobility service, on one
hand, will produce benets for the human being and the environment,
and on the other, it will also push vehicle manufacturers, rental com-
panies, and those who deal with charging infrastructure management to
start new projects in favor of increasingly sustainable mobility (Mounce
& Nelson, 2019). Furthermore, through sharing mobility, the problem of
the initial purchase cost of an electric vehicle could be overcome (which,
as highlighted in Section 3.2., is one of the weaknesses of electric
mobility) and the diffusion of electric vehicle circulation in urban areas
could be increased (which are also those with more congestion problems
and consequently emissions).
Sharing mobility could be accompanied by the adoption of con-
nected and automated vehicles (CAVs) (see e.g. Fagnant & Kockelman,
2015, Docherty et al., 2018). There are several examples in the world of
prototype services of shared automated vehicles. In Singapore, in 2016 a
eld trial of self-driving taxis began. The service is limited to an area of
2.5 square kilometers and requires the presence of a driver for emer-
gency situations in the initial phase. Waymo, (Google company) has
decided to bring its automated vehicles (AVs) on the road, with
ride-sharing services (service comparable to UBER with the difference
that the cars are self-driving). The services started in the city of Phoenix,
Arizona, where a eet of 500 new Chrysler Pacica equipped with the
autonomous system is available 24 h a day and 7 days, in a restricted
area of the territory (Fig. 10). The vehicle is booked online through the
pages of the ofcial website where the user will write the desired
destination. A centralized system will receive user requests, including
the position where the car must go to pick up the passenger, will check if
the route to be taken is completely safe for self-driving mode, possibly
suggest a starting point or different arrival, and will choose the best path
to take. However, current legislation does not yet allow self-driving cars
to drive in complete autonomy. And that’s why Chrysler Pacica plug-in
hybrids will have engineers able to take manual control of the vehicle at
any time if necessary.
Cruise, the General Motors-backed autonomous vehicle company,
currently offers its driverless cars to the public in San Francisco, even if
only for trips between 11 p.m. and 5 a.m., and mostly on the west side of
the city. Waymo will soon be offering service in San Francisco as well
(The New York Times, 2023).
Sharing automated vehicles (AVs) will bring huge gains in safety,
and the costs of transport to the user, the potential for automated ve-
hicles to reduce end-to-end travel times will have profound impacts on
society and the economy (Wadud et al., 2016).
Furthermore, AVs plus smart mobility will allow the capital stock of
the mobility system, primarily infrastructure and vehicles, to be used
Fig. 10. Waymo service in Arizona (sources: https://www.reuters.com/article/
us-waymo-autonomous-phoenix-idUSKBN26T2Y3).
E. Cascetta and I. Henke
Journal of Urban Mobility 4 (2023) 100059
13
much more efciently (Docherty et al., 2018).
The three drivers of the seventy revolution outline a vision of the
future in which mobility will be framed as a personalized ‘service’
available ‘on demand’, with individuals and rms having instant access
to a seamless system of clean, green, that takes advantage of new
technologies for efcient and exible transport to meet all of their needs
(e.g. Wockatz & Schartau, 2015; Docherty et al., 2018). The interaction
of the 3 innovation drivers could lead to “Sharing with Self-driving
electric vehicles”: self-driving and 100% electric cars, booked with
smartphones, picking up the costumer anywhere in the territory, used
and left wherever the users want and optimizing their recharging by
selecting the best station according to expected driving missions and
stations availability.
4. Sustainability and the seventh revolution
Sustainable development is dened by the United Nations Environ-
ment Programme (UNEP) as the capacity to satisfy current needs
without compromising future generations ones (FAO, 2020), managing
the earth’s resources and minimizing climate change’s negative impacts
(Session, 1987). Sustainable development is based on three fundamental
pillars: social, economic and environmental. In particular, environ-
mental sustainability entails improvement in the quality of the envi-
ronment and reduction of emissions and energy consumption
(greenhouse gasses emission variation; pollutant emission variation;
impact variation in other sectors) (Reisi et al., 2014; Shiau & Liu, 2013).
By contrast, social sustainability entails improvement in the quality of
life and social equity (e.g. easy access to transportation) and improved
safety (e.g. reduction in the frequency of accidents) (Reisi et al., 2014;
ECMT, 2004; Haughton, 1999). Finally, economic sustainability entails
making the mobility of people and goods more efcient and effective
and ensuring that the economic benets produced by the project (for the
period under survey) are greater than the costs (Zheng et al., 2015; Reisi
et al., 2014; Tien et al., 2020).
Environmental sustainability is arguably the most challenging to
meet given the strong dependence of virtually all transportation modes
on carbon-based fuels. The objective of net decarbonization by 2050 is
particularly difcult to attain in the transportation sector as resulted in
several studies (e.g. Kany et al., 2022; Dillman et al., 2021, Beatrice
et al., 2023). This led to classifying the transport sector as “hard to
abate” using UE terminology (The European House - Ambrosetti, 2022).
There is a wide agreement that de-carbonization objectives have to be
reached gradually and at different speed by different modes. As found in
many recent studies dealing with the decarbonization of the transport
sector (e.g., Emodi et al., 2022; Kramer et al., 2021; Kany et al., 2022;
Dillman et al., 2021), the ASI framework (Avoid, Shift, Improve) is the
most applied and promising approach in scenario building. This
framework is based on three principles (Arioli et al., 2020; Wilson et al.,
2020):
1. Avoid: reduce unnecessary polluting trips (fewer vehicle-kilometres
travelled for passengers and freights);
2. Shift: use less polluting transport modes (such as mass transit or
railways);
3. Improve: reduce pollution of vehicles within each mode (low carbon
energy technologies and/or emission optimized operations).
The three drivers of the possible seventh revolution are opportunities
to promote ASI policies and achieve the decarbonization aims promoted
by the EU. The possible contributions of the innovations underway as
well as their risks for environmental sustainability are displayed in
Table 2.
Specically, AVOID policies have the aim to reduce unnecessary
polluting kilometyers travelled.. Smart mobility could be an opportunity
to reduce vehicle- kilometres by increasing their occupancy. New dy-
namic ride-sharing systems have the potential to provide signicant
environmental benets by reducing the number of cars used for personal
travel and improving the utilization of available seat capacity (e.g.Agatz
et al., 2012). Some research (e.g. Cepeliauskaite et al., 2021; Sarker
et al., 2020; Kanchan et al., 2019; Canales et al., 2017) highlights the
positive impact of info-mobility on reducing unnecessary kilometres
travelled and so GHG emissions in the area. Specically, dynamic
trip-planning, on-demand mini busses, and real-time location-based
shared smart parking systems, produce benets in terms of reducing
kilometres travelled (Sarker et al., 2020; Kanchan et al., 2019) especially
in urban areas. The same is true for freight-as-a-service platforms such as
Flock Freight whose aim is to reduce truck-kilometres by optimizing
Less-than-a-truckload shipments (for details on Flock Freight see para-
graph 3.2). Obviously, other innovations have to potential to reduce
travel such as smart working, e-banking, e-learning, etc. These however
are not part of the transport revolution as dened in this paper.
SHIFTING the travel demand from transport modes with a high
environmental impact to more sustainable ones is a possible policy to
Table 2
Environmental sustainability: opportunities and risks.
OPPORTUNITIES RISKS
AVOID Smart mobility:
ride-sharing- increases vehicle
occupancy (e.g. carpooling,
FAAS),
info mobility- dynamic trip-
planning, on-demand minibuses
and real-time location-based
shared, smart parking systems,
could produce benets in terms of
reducing unnecessary kilometers
travelled
•Mobility levels may increase
due to the reduction of travel
costs and to the increase of
travel comfort;
•back-shift to individual
vehicles due to multitasking
possibility, lower driving
effort, less congestion, safer
trips with CAVs
•extra km travelled by
repositioning CAVs
•energetic mix of electricity
production.
SHIFT Transport decarbonization ±
Smart mobility
electric car/ scooter/ bike in
sharing could accelerate the shift
from internal combustion cars to
electric vehicles
Autonomous and connected
vehicles:
lower production costs and more
exibility for unmanned public
transportation could increase the
shift towards public transport
Smart mobility:
Maas- increased the travel quality
perception of public transport
could increase the shift towards
public transport
Smart pricing- Possibility of
travel demand management
schemes by smart pricing could
increase the shift towards
sustainable transport modes
IMPROVE Transport decarbonization:
new technologies could reduce the
environmental impact of trips
Autonomous and connected
vehicles:
The digital transformation of
vehicles and infrastructure will
allow
•reduction of fuel consumption
and pollutant emissions with
eco-driving capabilities
•possible increases in capacity
due to CAV on existing roads
and so the reduction of
congestion and related
emissions
Smart pricing could promote
lower energy consumption routes
and/or travel times
E. Cascetta and I. Henke
Journal of Urban Mobility 4 (2023) 100059
14
archive environmental sustainability. The new technology could make
more attractive and unconventional public transportation. An
improvement in the supplied service quality can attract further users and
cars would be used less (with effect on environmental sustainability).
MaaS has a positive impact on the perceived risk of the trips, on services
reliability, and on the perception of travel time, these dimensions have a
positive effect on travel satisfaction (e.g. ¨
Ozer et al., 2013; Dziekan &
Kottenhoff, 2007) and so on the number of trips by public transport
(Eboli & Mazzulla, 2012). The same could happen as a result of pro-
duction cost reduction following driverless public transportation (this
effect has already been observed in driverless metro systems). Further-
more, new smart mobility services could be implemented in low-density
areas. The shift towards greener transport modes can also be observed as
a result of a policy of the variation of the travel cost. The possibility of
travel demand management schemes by smart pricing and lower prices
for unmanned public transportation could affect a modal shift from car
to public transport (Cascetta et al., 2017).
Finally, sharing mobility with electric vehicles (electric car, scooter,
bike) could lead to increased mileage with low or zero emissions vehi-
cles as users can use electric vehicles, without having to buy one.
The policy of IMPROVE concerns all those policies that encourage
the use of new vehicles with low environmental impact and/or with
emission-optimized operations. The development of the new energetic
vectors (transport decarbonization driver) can contribute to a signicant
reduction in kilometers traveled in energy-intense vehicles, reducing
emissions from the transport sector (e.g. Kany et al., 2022).
Autonomous vehicles, with eco-driving capabilities, can produce a
reduction of energy consumption and pollutant emissions (e.g.Ma et al.,
2021; Igli´
nski & Babiak, 2017). On this topic, Truck platooning is ex-
pected to signicantly impact the road freight market and the environ-
ment. Some benets are unanimously acknowledged (Marzano et al.,
2022): platooning mitigates fuel consumption and emissions of green-
house gasses, by improving trucks’ aerodynamic performance.
Similarly smart pricing has the potential to promote lower energy
consumption routes and/or travel times (Cascetta & Montanino, 2022).
The three drivers of the possible seventh revolution are opportunities
to promote ASI policies especially in urban areas. In fact, there are
positive experiences around the world of how the transformation of
mobility services has quickly spread to urban areas and can be oppor-
tunities for sustainable development. For example, free-oating bike
sharing in Beijing (Wang & Sun, 2022; Sun & Ertz, 2021), free-oating
car sharing in some of European cities (such as Copenhagen, Rome,
Hamburg, and London) (Jochem et al., 2020), car sharing in London,
Madrid, Paris and Tokyo (Prieto et al., 2017) and in the Netherlands
(Nijland & van Meerkerk, 2017), diffusion of on-demand minibus ser-
vices in Melbourne (Liyanage & Dia, 2020), such as the Kutsuplus in
Helsinki (Haglund et al., 2019), autonomous vehicles in Valencia
(Zambrano-Martinez et al., 2019); Mobility as services in Helsinki (Jit-
trapirom et al., 2017) and the positive effects of the congestion pricing in
Jakarta (Sugiarto et al., 2020) are some examples of how new forms of
mobility can have positive effects in urban areas.
As for social sustainability, current innovations also have large
potential and pose new challenges, as shown in Table 3.
Technological innovations of the seventh revolution however may
also pose risks for environmental sustainability. For example, AVs in-
crease the attractiveness of traveling by car due to the reduction of travel
costs (e.g. European Commission, 2011; Bosch et al., 2016) and to the
increase in travel comfort (e.g. due to a reduction in driving efforts due
to CAV). This could lead to a backshift to individual modes and higher
equilibrium-levels of congestion (Gruel & Stanford, 2016). Gaio and
Cugurullo (2022) based on historical events and prevailing trends, have
identied in the autonomous and connected vehicles the possible risk for
the reduction of cycling, of public transportation, and other modes. In
fact, the development of AV requires infrastructure design and possible
development of urban areas focused on private transport and not on
other modes of transport such as the bicycle. Further to this,
Autonomous Vehicles–cyclist interaction is an important topic that
could lead to a reduction in cycling mobility with impacts on environ-
mental sustainability (Gaio & Cugurullo, 2022).
Furthermore, Spieser et al. (2014) and Fagnant and Kockelman
(2014) show, in two case studies, that a shared-vehicle mobility system
can satisfy the mobility demand of a city with signicantly fewer vehi-
cles while causing more vehicle km travelled for rebalancing the eet.
Similarly, Ride Pilot technology (described in Section 3.2) could
have the effect of increasing vehicle km travelled. The car can park (with
autonomous driving) in parking lots distant from the place where the
driver has abandoned the vehicle with extra miles for the positioning of
the vehicles with a trade-off between central parking spaces and kms
travelled (Stein, 2020). Finally, the risks to be taken into account in
policies to encourage electric vehicles concern the energy mix used to
produce electricity, not all of which have the same environmental im-
pacts (The European House - Ambrosetti, 2022).
Transport decarbonization and the reduction of local pollution (e.g.
particulate matter, acoustic, etc.) obtained with electric vehicles is a
clear advantage for air quality and public health, especially in cities with
high population density (Cartenì et al., 2020a).
In general, smart mobility and digital transformation can produce
positive societal effects such as : i) increase of safety (reduction of road
accidents due to human errors- Fagnant & Kockelman, 2015; Kockel-
man et al., 2016) ii) increase mobility opportunities for social groups
with now limited access (e.g. providing elderly and handicapped with
easily accessible way of moving around by themselves -Imai, 2019); iii)
enhance social cohesion in cities (e.g. with dedicated services for the
suburbs -Kopnina, 2017; Laamanen et al., 2015); iv) reduce travel times
(due to the reduction of congestion and new mobility services) and so
Table 3
Scocial and economic sustainability: opportunities and risks.
IMPACT OPPORTUNITIES RISKS
Social Transport decarbonization
Reduce air pollution and improve
public health
Autonomous and connected
vehicles ± Smart mobility
•increase of safety
•increase mobility opportunities
for social groups now limited
•increase new costumer-tailored
transportation services
•enhance social cohesion in cities
•reduce travel times increasing
time available for other non-
driving/travel activities
•increase in public spaces in cities
due to the reduction of parking
needs
•increase the attractiveness of
traveling by car leading to a
higher level of congestion and
related pollution with negative
effects on public health
•increases in social inequity due
to the higher cost of electric
and/or CAVs
•reduction of driving jobs
related passengers and freight
vehicles
Economic Transport decarbonization
the reduction of transport costs due
to reduced energy costs (e.g.
renewable energy costs if the case)
Autonomous and connected
vehicles ± Smart mobility
•increase revenues and job
opportunities for different sectors
(e.g. data services, digital media,
electronics, and software)
•new business opportunities in the
mobility services industry
•cost reductions for road transport
commercial operations (due to fuel
consumption and travel time
decreases with truck platooning
and cost saving due to driver time
restrictions not being applied)
•increases of capacity for existing
infrastructures and reduced
demand for new roads
•possible increases of vehicle
production costs due to
monopoly of raw materials
needed in the transition to
electric vehicles
E. Cascetta and I. Henke
Journal of Urban Mobility 4 (2023) 100059
15
increases in time available for other non-driving/travel activities (Wang
et al., 2017; Dresner & Stone; 2004; Fajardo et al., 2011), v) increases in
public spaces in cities due to the reduction of parking needs (e.g. relo-
cation of parking spaces for autonomous vehicles-Stein, 2020). Also
freight transport, truck platooning is expected also to ameliorate trafc
safety (Segata et al., 2015; Zheng et al., 2015; Tsugawa et al., 2011).
There are also risks for the social sustainability of the seventh rev-
olution. The performances (social impact) of digital transformation of
infrastructures are strongly inuenced by the technological develop-
ment of vehicles which will evolve towards automatic driving and vice
versa. The positive effect on road accidents of CAVs will be seen with
high penetration rates, while in the transition phase the effect could be
marginal given the coexistence with traditional vehicles (e.g. E. Cas-
cetta et al., 2022; IIA, 2021). At the same time, there is a risk that digital
transformation will signicantly increase the attractiveness of traveling
by car and, consequently, trafc volumes will rise signicantly, leading
to a higher equilibrium-level of congestion (e.g. Gruel & Stanford,
2016). Among the possible social risks there is reduction of the labor
force in some sectors due to automated driving and related transition (e.
g. Kropp & Dengler, 2019). Furthermore, increasing the use to electric
vehicles Section 3.2 and the circulation limitations to older and more
polluting vehicles, may involve increases in social inequity. As a matter
of fact higher purchasing costs of BEV w.r.t. ICE vehicles, possibly
combined with restrictions to the latter, favour higher income groups
and regions. This thesis is supported by the results of the regression
analysis shown in Fig. 11. In particular, different regression models (e.g.
linear, multiple, exponential, logarithmic, polynomial) were tested to
evaluate if there is a correlation between BEV penetration on new car
sales and GDP per capita. The percentage of new BEV registrations w.r.t.
the number of total new registrations was estimated for 20 European
Fig. 11. Electric vehicle adoption and Pro-capita GDP correlation for EU country and Italian Region.
E. Cascetta and I. Henke
Journal of Urban Mobility 4 (2023) 100059
16
countries (Italy, Belgium, Croatia, Denmark, Estonia, France, Germany,
Greece, Hungary, Lithuania, Holland, Poland, Portugal, Romania,
Slovakia, Slovenia, Spain, Sweden, Iceland, Norway) and for the 21
Italian regions (Abruzzo, Basilicata, Calabria, Campania,
Emilia-Romagna, Friuli-Venezia Giulia, Lazio, Liguria, Lombardy, Mar-
che, Molise, Piedmont, Puglia, Sardinia, Sicily, Tuscany, Trentino Alto
Adige, Umbria, Valle d’Aosta and Veneto) based on ofcial data (
EUROSTAST and ACI ) for the years 2019 and 2022 respectively. The
regression results (Fig. 11) show that there is an exponential correlation
for these countries/regions between BEV penetration on new car sales
and GDP per capita.
These results suggest that policies that limit access to non-electric
vehicles can increase social inequalities in terms of accessibility.
Similar results are found in other research (e.g. Sierzchula et al., 2014;
Tu & Yang, 2019; Ruoso & Ribeiro, 2022) and also in relation to ADAS
supported cars.
Also for economic sustainability, ongoing technologic innovations
present risks and opportunities (Table 3). The opportunities concern the
reduction of transport costs due to reduced energy costs (e.g. renewable
energy costs and mechanical efciency of electric motors) and driving
costs (autonomous driving) for passengers and freights (e.g. Gruel &
Stanford, 2016). The practice of sharing promises to provide an oppor-
tunity to save and/or make money (Fang et al., 2016; Heo, 2016) and in
general to facilitate sustainable economic growth (Bonciu & Balgar,
2016). Clements and Kockelman (2017) through a quantitative
approach, examine the socioeconomic effects of the introduction of AVs
in the United States considering many economic sectors (e.g. the auto-
motive industry, electronics and software technology, trucking and
freight movement, personal transport, auto repair, medical services,
legal assistance, construction and infrastructure, land development,
digital media, oil and gas). In their research, they provide an estimation
of economy-wide effects that could lead to an increase of 8% of US gross
domestic product (GDP). Due to the diffusion of new technologies is
expected an increase revenues from different sector such as data ser-
vices, digital media, electronics and software (Alonso Raposo et al.,
2018). New services linked to vehicle automation and connectivity will
increase revenues from data services and increased demand is expected
in sensors, controllers, actuators, self-driving software, maps, etc. that
will be required for automated driving. Manufacturing and service in-
dustries, both big and small and medium-sized enterprises (including
start-ups), will also prot from new business opportunities that concern
automation and robotics, services for citizens’ mobility, and new
transport services, (European Economic and Social Committee, 2017).
Revenues from road transport commercial operations could increase
as fuel consumption and travel time decreases with truck platooning,
number of truck drivers needed decreases (even if wages could increase
with a more technical role, e.g. monitoring the CAV) and if driver time
restrictions no longer apply (Alonso Raposo et al., 2018, European
Economic & Social Committee, 2017).
Furthermore, it is expected increases in capacity for existing in-
frastructures (with smart roads and CAVs) (e.g. Diakaki et al., 2015),
demand for new roads construction could instead decrease saving
expansive new motorways construction projects. Other possible eco-
nomic risks related to the decarbonisation of transport concern the
possibility of increases in production costs due to semi-monopolistic
control of minerals such as rare earth and related geopolitical conicts.
These effects are the ones currently perceived by experts in this rst
stages of the seventh revolution and at the time is not known how is
going to be the nal balance on sustainability between opportunities and
risks
In addition, there will be impacts that at this early stage are very
difcult, if not impossible to anticipate as already happened for other
revolutions in history. This may include the shape of cities, the same
structure of inter-personal relationships and the society as well as the
production/ distribution cycles. These are the kind of effects that follow
from the law of “unintended consequences” pertaining to revolutions.
Only time will tell.
5. Conclusions
In this paper, we presented the technological innovations that are
affecting virtually all aspects of transportation systems under a holistic
perspective. Transport revolutions, produced by a combination of
technological innovations and related organizational ones, affect the
structure of society in signicant and often unpredictable ways in a
relatively short period of time. Under this perspective, the trajectories of
innovation in separate areas, such as new energy vectors to decarbonize
transport, autonomous driving, and innovative mobility services
converge and interact. Their interactions and evolutions will dene the
land-scape of future mobility systems for people and freight and prob-
ably will change the structure of our society, as it has been the case in a
few corresponding historical circumstances. In this paper, after
analyzing the past and the 6 revolutions that occurred in remote and
recent history, we analysed what are the current developments of the
above innovations and some of the potential effects, either desirable or
not, they could have on environmental, social, and economic sustain-
ability of the transportation system.
Focusing on the possible impacts of the seventh revolution on urban
areas, history shows that changes in transport produced changes in
urban design and (e.g. Mumford, 1961; Lynch, 1964) therefore changes
in sustainability (Cugurullo et al., 2021). For examples, the spread of
AVs could lead to a reduction in car ownership and therefore the number
of cars in the city could potentially decrease. Consequentially, many
urban spaces currently meant for automobiles could become obsolete,
thus becoming prone to being repurposed as bike paths, gardens and
public places which would increase urban sustainability. However, the
development of very comfortable cars driven by an articial intelligence
that promises onboard productive and recreational activities, it could
increase the demand for cars, and therefore the amount of urban spaces
and energy that is needed to sustain them. So, it is therefore important
that decision-makers in the near future know the risks and opportunities
of the seventh revolution and can make rational decisions in order to
exploit the opportunities and keep the risks under control.
The challenge for transport policy -makers will be to imagine
possible futures and making decisions in a context of deep uncertainty
(e.g. Marchau et al., 2019). Deep uncertainty is referred to one in which
there are a virtually innite number of possible future scenarios without
the possibility to give a-priori probability to them.The uncertainty
sources at this point are related to (i)demand, (e.g. socioeconomic var-
iables related to travel demand, users’ trip and travel behavior); ii)
supply (e.g. supply performances, technological disruptive innovations)
and iii) context (e.g. societal values and preferences, global and local
regulations) (Cartenì et al., 2022). The new decision making challenge
will require new approaches to the task allowing open and dynamic
settings as well as the structuring as dynamic processes (e.g. Cartenì
et al., 2022). In addition to knowing the opportunities and risks of the
seventh revolution, public participation is essential to imagine and
create a sustainable future of urban transport and mobility, given the
intrinsically political nature of the process. To this end, Acheampong
et al. (2023) proposed a multi-criteria visioning and appraisal frame-
work and methodology to help the decision makers to envision the role
of new mobility in the future.
To this end, scientic research can help shape the future of mobility
The perception of the current time as a revolutionary phase should
change the approach of researchers and practitioners in the wide eld of
transportation system analysis with respect to the last evolutionary de-
cades. Future research, in addition to sector specic evolutions, should
focus on the actual holistic deployment of the seventh revolution trying
to continuously update its combined effects and anticipate as much as
possible its trajectory in order to reduce undesirable ones while boasting
desirable ones
E. Cascetta and I. Henke
Journal of Urban Mobility 4 (2023) 100059
17
Declaration of Competing Interest
The authors whose names are listed immediately below certify that
they have NO afiations with or involvement in any organization or
entity with any nancial interest (such as honoraria; educational grants;
participation in speakers’ bureaus; membership, employment, consul-
tancies, stock ownership, or other equity interest; and expert testimony
or patent-licensing arrangements), or non-nancial interest (such as
personal or professional relationships, afliations, knowledge or beliefs)
in the subject matter or materials discussed in this manuscript.
Acknowledgments
This study was developed within the Spoke 8—MaaS and and
Innovative services of the National Center for Sustainable Mobility
(MOST) set up by the “Piano nazionale di ripresa e resilienza (PNRR)—
M4C2, investimento 1.4, “Potenziamento strutture di ricerca e creazione
di “campioni nazionali di R&S” su alcune Key Enabling Technologies”
funded by the European Union.
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