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Today’s urban transport systems are dominated by private vehicles, which are significant contributors to traffic congestion and pollution. This is expected to increase as the urban population grows, predicted to account for about 68% of the world’s population by 2050. In comparison to private cars, transport systems dominated by buses produce lower traffic congestion and emissions. Therefore, improvements in bus operation activities most of which require information on bus location (i.e. location based services) should facilitate urban transport sustainability. However, to date there is no agreement globally on the location based services, their location requirements and technologies to deliver significant improvement in bus operations. Therefore, this paper creates for the first time, a comprehensive list of bus operation services and specifies the performance requirements. These are considered together with challenging spatio-temporal characteristics of the urban environment to specify a high-level location determination system architecture for urban bus operations. The services, their requirements, standards and positioning system architecture are essential for the formulation of appropriate policies, regulation, service provision, and development and procurement of urban bus positioning systems.
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Case Studies on Transport Policy
journal homepage: www.elsevier.com/locate/cstp
Urban bus positioning: Location based services and high level system
architecture
Mireille Elhajj
a
, Washington Yotto Ochieng
b,
a
Astra-Terra Limited, United Kingdom
b
Head of the Centre for Transport Studies, Imperial College London, United Kingdom
ARTICLE INFO
Keywords:
Urban
Buses
Congestion
Location based services
ABSTRACT
Todays urban transport systems are dominated by private vehicles, which are signicant contributors to trac
congestion and pollution. This is expected to increase as the urban population grows, predicted to account for
about 68% of the worlds population by 2050. In comparison to private cars, transport systems dominated by
buses produce lower trac congestion and emissions. Therefore, improvements in bus operation activities most
of which require information on bus location (i.e. location based services) should facilitate urban transport
sustainability.
However, to date there is no agreement globally on the location based services, their location requirements
and technologies to deliver signicant improvement in bus operations. Therefore, this paper creates for the rst
time, a comprehensive list of bus operation services and species the performance requirements. These are
considered together with challenging spatio-temporal characteristics of the urban environment to specify a high-
level location determination system architecture for urban bus operations. The services, their requirements,
standards and positioning system architecture are essential for the formulation of appropriate policies, regula-
tion, service provision, and development and procurement of urban bus positioning systems.
1. Introduction
The worlds population is predicted to grow signicantly over the
next decades (UN, 2017; UN 2018). Currently 55% of the worlds po-
pulation (4.2 billion) lives in cities. By 2050, the urban population will
reach nearly 5.9 billion representing approximately 68% of the worlds
population, with 90% in cities in Africa and Asia. By 2030, the world is
projected to have 43 megacities of more than 10 million inhabitants
most in developing countries (UN, 2018). Without appropriate mea-
sures, the predicted surge in population is likely to be accompanied by
increasing trac congestion, air pollution, sprawl, energy use and
pollution, presenting signicant challenges to sustainability.
To address the problems associated with population growth and its
impact on the transport infrastructure, improvement in bus systems
(and hence operations) in the developed and developing cities has a
potential as a cost-eective approach to facilitate the achievement of
transport sustainability. In comparison to private-vehicle dominated
urban transport systems, those that are largely reliant on buses produce
signicantly less congestion, lower energy consumption and emissions.
This is because buses when full are inherently ecient both in terms of
road space and fuel consumption per passenger kilometre. Depending
on the type of bus (standard, articulated, bus-train or double articu-
lated), a fully occupied bus can replace between 5 and 40 cars with a
corresponding fuel saving ranging from 40 to 97% (UITP, 2015).
Furthermore, relative to the other public transport modes, buses are
already the most widely used. For example, in the European Union in
2014, 57.6 billion passenger journeys were made using public transport
of which 55.8% used buses, with metro systems accounting for 16.1%,
tramways or light rail 14.5%, and suburban railway 13.6% (UITP,
2016). These potential benets have spurred a speedy evolution in bus
and related technologies, infrastructure, concepts of operation, business
models and operations best practice or benchmarking, with increasing
evidence that buses are a very appropriate mode to meet sustainability
requirements. This is in terms of energy eciency, emissions, space
occupancy as well as operational eectiveness as buses are more easily
adapted to passenger requirements and do not require heavy infra-
structure. This is in addition to safety benets as bus accident rates are
relatively low compared to other surface modes. Particular areas of
improvement have involved movement away from diesel and biodiesel
buses, by far the largest part of urban bus eet (90% in Europe) towards
alternative fuels (e.g. hydrogen) and electric buses, development of new
engines to comply with Euro VI diesel, accelerated bus renewal and
https://doi.org/10.1016/j.cstp.2020.01.004
Received 15 November 2018; Received in revised form 15 November 2019; Accepted 18 January 2020
Corresponding author.
E-mail addresses: mireille.elhajj@astra-terra.com (M. Elhajj), w.ochieng@imperial.ac.uk (W.Y. Ochieng).
Case Studies on Transport Policy 8 (2020) 12–21
Available online 22 January 2020
2213-624X/ Crown Copyright © 2020 Published by Elsevier Ltd on behalf of World Conference on Transport Research Society. All rights reserved.
T
substitution of old buses, and operations infrastructure including ITS
(UITP, 2015; Tozzi et al., 2016).
On infrastructure, there is a move towards segregated bus transport
systems and improvement of intermodal connectivity (e.g. rail stations
with fully closed or sheltered bus stops and direct connections to pe-
destrian and cycle routes). In addition, conventional bus stops are
transforming into new bus shelters with CCTV and real-time display to
increase security (and therefore, customer satisfaction rate) and service
reliability. In cities, to reduce emissions, many roads are being re-
allocated to allow only public transportation (and cycles) to alleviate
trac congestion and improve reliability. Other considerations include
highway priority measures, enhanced passenger waiting facilities and
pedestrian/cycling improvements in addition to higher eciency trac
signal signalling technology (Arup, 2019).
In onboard sensing, there has been a move away from reliance on
bus drivers to detect pedestrians, cyclists, and motorcyclists, to using
sensor technologies including radar and optics. Examples are CycleEye
and Cycle Safety Shield. The former is an advanced cyclist detection
technology which uses both radar and optical technologies to detect
cyclists proximate to vehicles audibly alerting the bus driver to their
presence. Cycle Safety Shield detects pedestrians, cyclists or motorcy-
clists proximate to vehicles, giving a visual warning and then an audible
alert to the driver (Transport for London, 2014). Another example
technology is the mobile eye with the capabilities for forward collision
warning, pedestrian and cyclist collision warning, headway monitoring
and warning, lane departure warning, speed limit indicator and trac
sign recognition (SSMT, 2019). Other developments include automated
passenger counting.
In operations, the key aspects of the operational strategies for buses
include bus departure timetable management and bus stopping man-
agement. There has been a move away from a xed departure time
strategy or a strict time-based scheduling system to a exible headway
management strategy. Either the departure time is exible and can
reduce the possibility of clustering or a hybrid strategy is used where
both departure and en-route management are eected (IBI, 2018;
Chow, 2019). In bus stopping management, the conventional method is
full route operation where every bus stops at every single stop, making
the total journey time longer. There are several new operational stra-
tegies for bus stopping. Firstly, short turn refers to early termination of
the bus. Secondly, limited stops refers to the skipping of certain stops
with lower passenger volume. The combination of these two could re-
duce the bus travel time (Tang et al, 2018). In addition, Intelligent
Transport Systems (ITS) are increasingly being used to provide amongst
others Real-Time Passenger Information (RTPI) (Bullock et al., 2005;
Eken and Sayer, 2014; Feng et al., 2018; Kunder et al 2019).
The evolution in buses summarised above are particularly evident in
Europe where there have been several specic initiatives as described
briey below.
The Zero Emission Urban-bus Systems (ZeEUS) project aims to ex-
tend fully-electric solution to the core part of the urban bus network
composed of high capacity buses. ZeEUS adopts pure electric and
electric-diesel hybrid engines, and it combines intelligent transpor-
tation systems technology, priority at junctions, and rapid and
convenient fare collection, and is integrated with land-use policy in
order to substantially upgrade bus system performance. The ad-
vances of ZeEUS include diesel fuel savings, and reducing CO
2
emissions, greenhouse gas (GHG) emissions, SO
2
emissions, noise
pollution as well as other pollutants.
The European Bus-system of the Future (EBSF) Phase I and II pro-
jects (EBSF) aim to develop a new generation of urban bus systems
by means of new vehicle technologies and infrastructure in combi-
nation with operational best practices. EBSF adopts energy man-
agement strategies based on both real-time and anticipation of the
near future operating prole. Intelligent transport system-based
operations are used to optimise interfaces between vehicle and
platform to improve accessibility. The advances of EBSF include the
environment (clean, low noise and zero emissions), ease of the ow
of passengers and optimisation of dwell time.
The Intelligent, Innovative, Integral Bus Systems (3iBS) project aims
to develop a Roadmap for European Advanced Bus Systems re-
search, dene a work-plan for the exploitation of research results,
and transfer most innovative concepts to a wider audience in
Europe. 3iBS investigates electrication, compressed natural gas,
biodiesel and biogas solutions. Furthermore, it realises a variable
capacity by coupling and uncoupling single buses or trailers to
buses. 3iBS uses plug /un-plug bus modules both during operation
and at the bus depots in the kinematic chain of the vehicle.
The Electrication of Public Transport in Cities project (ELIPTIC)
aims to develop new concepts and business cases in order to opti-
mise existing electric public transport infrastructure and rolling
stock, saving both money and energy. ELIPTIC uses battery and
hybrids charged buses, and they charge e-buses en-route(e.g.
trolleybus operated on tram infrastructure) or on the spot (battery
buses, hybrids charged from trolleybus, tram, metro network). The
benets include reduced fossil fuel consumption, improved local air
quality through reduced local emissions, and advocating for an
electric public transport sector at the political level.
The OPTICITIES project partners from Sweden (OPTICITIES): Volvo,
Chalmers, Göteborgsstad, and Västtrak, aim to develop genuine
multimodal solutions based on reliable data for every mode and a
combination instead of a juxtaposition of mono-modal approaches
exclusively focused on public transport. OPTICITIES use multimodal
and predictive management to optimise urban networks.
The Viajeo PLUS project (Viajeo) aims to facilitate active uptake and
transfer of knowledge and solutions, and provide the framework for
the identication of the best solutions and eective mobility man-
agement. The main advance of Viajeo PLUS is to provide urban ci-
tizens with the best possible journey conditions and to optimize
urban logistics operations
The New tools for design & operation of urban transport inter-
changes (NODES) is a collaborative project co-funded by the
Seventh Framework Programme focused on building and testing a
Toolbox to support European cities in the design and operation of
new or upgraded urban transport interchanges. NODES integrates
land use planning with urban passenger infrastructure planning for
the design of ecient transport interchanges.
The Information Technology for Public Transport (ITXPT) project
aims to provide public transport authorities and operators with re-
commendations and requirements to support the purchase and in-
tegration of interoperable IT architecture. ITXPT denes interoper-
ability on hardware, communication protocol, and service levels,
and it integrates modules in a coherent architecture to simplify ac-
cess to the market.
The European Road Transport Research Advisory Council (ERTRAC)
project aims to accelerate road transport research to deliver sus-
tainable road transport and mobility of goods and passengers, the
environment and European competitiveness. The main advances of
ERTRAC include facilitating the exchange between cities on urban
electric mobility solutions.
CIVITAS is a network of cities dedicated to cleaner, better transport
in Europe and beyond. Its main features are: electric and diesel
engines, biofuels including biodiesel, biogas and compressed natural
gas. The main advances of CIVITAS include the innovative and
sustainable delivery solutions for goods and services.
While the initiatives above, have the potential to improve bus op-
erations, none addresses in a exhaustive way, the signicant opera-
tional element that relies on Location Based Services (LBS) such as es-
timated time of arrival, bus priority at junctions, and in-situ (dynamic)
environmental compliance monitoring. Conventionally, within ITS for
location based services for bus operations, Automatic Vehicle Location
M. Elhajj and W.Y. Ochieng Case Studies on Transport Policy 8 (2020) 12–21
13
(AVL) is responsible for the determination of the position, location and
derivative information such as estimated time of arrival, while
Automatic Vehicle Management (AVM) uses the AVL and other in-
formation to manage vehicles, drivers and service (e.g. regularity,
compliance with schedule and data capture). Although there have been
some studies on such systems and their applications (e.g. Parker, 2008;
Porretta and Milner, 2009; Hounsell et al. 2012, SaPPART, 2015;
Manela, 2016, Tilocca et al. 2017), they focus on specic aspects with
the consequence that there is no agreed exhaustive and comprehensive
list of applications or services for urban bus operations. Furthermore,
the location requirements in terms of the four Required Navigation
Performance (RNP) parameters of accuracy, integrity, availability and
continuity are still to be specied and agreed.
Accuracy is a measure of the conformance of an estimated position
solution to the true position at the 95th percentile. Integrity relates to
the trust that can be placed in the correctness of information supplied
by a navigation system. It includes the ability of the system to provide
timely warnings to users when the system fails to meet its stated ac-
curacy. Specically, a navigation system is required to deliver a
warning (alarm or alert) when the error in the derived user position
solution exceeds an allowable level (alert limit). This warning must be
issued to the user within a given period of time (time-to-alert) and with
a given probability (integrity risk). Continuity is the capability of a
system to provide the required levels of accuracy and integrity during a
period of operation. Continuity risk is therefore, the probability of
unscheduled interruptions during the operation. Availability is the
proportion of time that a service is provided with the required levels of
accuracy, integrity and continuity (Ochieng et al., 2003a,b).
The RNP forms the basis for the specication of location determi-
nation technologies to deliver signicant improvement in bus opera-
tions and hence ridership. Because of this lack of the RNP, the current
location determination systems are inadequate with low levels of per-
formance in terms of, for example, accuracy and integrity of travel time
estimation. Therefore, to specify ecient technologies for location de-
termination, it is imperative in the rst place to identify the location
based services and their RNP targets (Porretta and Milner, 2009).
Given the above limitations, the motivation for this paper is to
contribute to the improvement of the eciency of urban bus operations
in three ways. Firstly, it identies and denes a comprehensive list of
location based services. Secondly, the RNP for each of the applications
is specied. Thirdly, the RNP is used to specify a high-level architecture
for the location determination system. The rest of this paper is struc-
tured as follows. Section 2 identies and describes the LBS relevant to
urban bus operations and creates the rst comprehensive list. The RNP
for the applications is addressed in Section 3. The applications and their
requirements are used to inform the specication of a high-level urban
bus positioning system architecture in Section 4. The paper is concluded
in Section 5.
2. Location based services
Literature review and a dedicated survey are employed to identify
and dene the Location Based Services (LBS) relevant to urban bus
operations. The limited literature on this subject is in part addressed in
the second instance by a dedicated international survey of the main bus
operators to corroborate and augment the services determined from the
literature review.
2.1. Literature review
Parker (2008) through a combination of literature review and sta-
keholder survey presents a useful analysis of the applications, tech-
nologies and benets of AVL systems for bus transit. The benets are
discussed within the contexts of operations, maintenance, customer
service, security, information technology, planning, revenue, marketing
and training and human resources. The survey undertaken addressed
the three areas of (i) technologies, timing and scale of implementation,
(ii) issues experienced when designing, procuring, implementing, in-
tegrating and using AVL systems, and (iii) lessons learnt. Porreta et al
(2009) identies and presents the various location based services that
could be supported by Global Navigation Satellite Systems (GNSS) such
as the Global Positioning System (GPS). This is done within the context
of a wider research on the impacts and mitigation of interference (in-
tentional and non-intentional) with GNSS signals through jamming,
spoong, meaconing and signal attenuation. Hounsell et al. (2012),
explore the management of large data generated by AVL systems with a
case study of the Londons iBUS system. They illustrate this with three
online and two oine applications. SaPPART (2015) explores the issues
of specication of the location determination requirements and testing
of systems that provide positioning, velocity and timing information for
a number of services that are provided through ITS. Manela (2016)
focuses on the applications and technological solutions to urban bus
operations that are underpinned by the Londons iBUS system. Tilocca
et al. (2017) assesses the uses of real-time and non-real time functions
based on AVL data with a case study of Cagiaris AVL system. The real-
time applications are presented within the contexts of eet manage-
ment and operations, bus priority at trac signals, information for
passengers. The non-real time applications are in the contexts of per-
formance measurement, service planning and operations.
The limitations of the studies above is that they focus on aspects of
AVL, without a holistic consideration of urban bus location based ser-
vices, the required navigation performance and the corresponding po-
sitioning architecture. Therefore, an initial attempt is made below to
consolidate (from the studies) the various location based services. From
the existing sources, Table 1 presents the location based services re-
levant to urban bus operations together with the relevant stakeholders.
Each is described in turn briey below.
Table 1
Urban bus operations LBS literature review.
Service User group or stakeholders
Travel Time/Real Time Passenger Information (RTI) Bus riders, travel and trac management and operators.
Service control Operators
Bus priority at junctions/selective vehicle priority Travel and trac management and operators.
Low bridge alarms Riders, drivers, trac management and operators
Headway Operators, trac management and operators
Bus Lane enforcement Trac enforcement
Dynamic route guidance/ Navigation Bus drivers, bus operators
Fleet management Bus operators and managers
Intelligent Speed Assistance Bus driver, bus operators /managers
Collision avoidance Riders, Bus operators and managers, Fleet asset owners
Emergency/Incident management Bus operators and managers, Fleet asset owners
Lane Control Bus drivers and operators
Restraint Deployment Bus drivers and operators
Performance measurement/operations monitoring Bus drivers and operators
M. Elhajj and W.Y. Ochieng Case Studies on Transport Policy 8 (2020) 12–21
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Travel time/Real-Time Passenger Information: This involves the esti-
mation of bus travel time to various bus stops pre-trip with the neces-
sary dynamic updates during trips. This service is crucial for bus riders,
bus drivers and bus monitoring authorities. The information is com-
municated to the various stakeholders via appropriate media. This
service underpins the Real Time Information (RTI) delivered to pas-
sengers via applications (apps), the internet, Short Message Service
(SMS), countdown signs and on the bus.
Service Control: This service is used by bus operators for the man-
agement of headways and o-line intervention. It relies on the eet
management service functionality.
Fleet Management: This application is for eet operators to optimise
their operations (i.e. keep operations as normal as possible) particularly
during incidents/accidents, and other safety and security related oc-
currences.
Bus priority at junctions/selective vehicle priority: This is required to
enable seamless movement of buses through signalised junctions by
giving priority to buses over other trac. The buses transmit their
position to a control centre which locates them on the road network and
provides signal priority. This could be done for example, by extending
or pre-empting the green phase at trac lights, thus creating a green
wave to signicantly reduce delays. Such a service could be applied all
the time, but more practically only for late running buses.
Low bridge warning: This service uses real-time bus tracking and low
bridge position data together with communication methods to warn the
driver that the bus is approaching a low structure or bridge. This is
particularly crucial when a vehicle is diverted from its normal route (in
service or while returning to the depot/garage).
Headway: The headway indication system in the bus cab displays
information on the destination of the bus and the next stop for travelers
not close to a sign on the road but close to a bus. In case of any change
in the route, this service conveys the relevant information on the
change.
Bus lane enforcement: This service uses bus positioning information
to monitor and enforce adherence to bus lanes by bus drivers.
Furthermore, bus drivers can report to service control, unauthorised
vehicles using radio communications.
Dynamic route guidance/navigation: In this application, drivers input
the location of the destination and in some cases the details of the
preferred route into the on-board navigation computer which matches
the current position estimation of the bus with the digital map via map
matching (i.e. the use of an objects coordinates to determine its phy-
sical location on a map) (Ochieng et al., 2003). The navigation system
then provides turn-by-turn navigation instructions and visual display to
reach the required destination. Real time trac information can be
input as well as other factors that aect trac such as weather condi-
tions and forecasts, incidents/accidents and blocked roads.
Intelligent speed assistance: This application detects rstly through
the position of a vehicle, and a Geographical Information System (GIS),
the relevant attribute in terms of the speed limit on a link, and then
compares it to the driving speed. It can either issue a warning or invoke
a speed limiter if implemented or both. The link determination is
through a positioning system and GIS where the former is responsible
for the determination of the coordinates of the vehicle and the GIS is
responsible for the physical location of the link and its speed limit.
Collision Avoidance: This application detects proximate tracto
avoid potential collisions in parking garages as well as en-route. A high
update rate of position and velocity estimations could be used to detect
and avoid collisions. The service works on the estimation of the states of
the relevant vehicles and derivation of velocity and intervention
through speed adaptation and/or warning the drivers. This application
currently mostly uses camera/radar technology to issue warnings.
There is the potential for GNSS (potentially augmented with the
camera/radar) to be used particularly for enhanced collision avoidance
between equipped vehicles.
Emergency/Incident Management: The requirements in terms of the
location determination for a bus in distress is dictated by the needs of
the emergency services in maximising the benets of a response, e.g.
reducing the impact of injuries or fatalities related to journeys.
Lane control: In this service, positioning, navigation and timing in-
formation are used to aid drivers in lane keeping in dierent types of
roads, to improve safety. Lane keeping involves detection of irregular
driving and raising alerts, and potentially automated intervention.
Environmental monitoring: With the increasing availability of low-
cost portable Emissions Monitoring Systems (PEMS), this service would
support such systems for in-service emissions monitoring by enabling
the spatio-temporal referencing of the emissions data. Such data are
useful in developing emission models and feedback to drivers on best
driving practice to reduce emissions.
Restraint Deployment: In the case of an unavoidable collision, re-
straints are deployed for the safety of vehicle occupants, resulting in the
reduction of the severity of accidents. In the event of incorrect deci-
sions, this application could have signicant safety and legal implica-
tions, hence, the required performance are inevitably stringent.
Performance measurement/operations monitoring: The data collected
and the information gathered feeds into measuring and quantifying the
Quality of Service Indicators (QSI) and performance monitoring
models. QSI such as the Excess Waiting Time (EWT), lost mileage and
driver compliance with schedules, form the basis for contracts granted
to operators in some jurisdictions.
From the applications or services in Table 1, it is notable that there
is no function dedicated to the real-time monitoring of the quality or
integrity of the navigation solution, a key requirement for mission (e.g.
safety) critical applications.
2.2. Survey of bus operators
In discussions with Transport for London, a questionnaire survey-
based study was commissioned in 2016 and undertaken by the
Transport Strategy Centre (TSC) within the Centre for Transport Studies
(CTS) at Imperial to conduct a condential survey to capture the state-
of-the-art in automatic vehicle location-based bus operations in 14
major cities around the world including London. The initiation of this
study recognised that high performance bus location facilitated delivery
of ecient operations.
The cities surveyed are members of the International Bus
Benchmarking Group (IBBG), which is facilitated by the RTSC at
Imperial College London. The IBBG is now in its twelfth year. Its current
members are TMB Barcelona, STIB Brussels, Dublin Bus, IETT Istanbul,
Rapid Bus Kuala Lumpur, Carris Lisbon, London Buses, STM Montreal,
NYCT and MTA Bus New York, RATP Paris, KCMT Seattle, STA Sydney
Buses, Singapore SMRT and CMBC Vancouver. All provide regular
passenger public bus service operations in large urban areas. To date
more than 15 years (20012015) of data, are available for 105 key
performance related items to create Key Performance Indicators (KPIs)
in areas such as service availability, accessibility, reliability and quality,
productivity, nance, safety and security, growth, learning and en-
vironmental performance. It took three years of iterative denition
development to ensure comparability of the members. The fteen or-
ganizations of the IBBG are all urban bus operators in large cities. The
original eight member organizations, from seven countries, were
chosen on the basis of their similar characteristics. One of the selection
criteria was eet size, which was set to be 1000 or more buses. Other
criteria were similarities in the service characteristics, technological
comparability, and the role of the operator within the city (Trompet
et al, 2009; Trompet et al, 2018).
It was envisaged that the results of this survey would augment those
of the literature review to compile a comprehensive list of LBS, dene
the corresponding performance targets and specify a bus location de-
termination system architecture that meets the current and future LBS
requirements. Therefore, the objective of the survey was to capture and
analyse data on the current and future LBS, the performance measures
M. Elhajj and W.Y. Ochieng Case Studies on Transport Policy 8 (2020) 12–21
15
and related monitoring methods and practices, and the current bus
location determination systems. In line with this objective, and fol-
lowing consultations/piloting between the RTSC and the IBBG, the
survey questions were formulated to capture: (i) the current stake-
holders and the relevant applications, (ii) the dierent performance
measures or indicators and the corresponding quantied targets (in-
cluding the RNP), (iii) the dierent practices for tracking and mon-
itoring performance, and detection and mitigation of performance de-
gradation, (iv) the future LBS and the corresponding performance
measures and targets, and (v) the bus location technologies used.
The questionnaires were dispatched to urban bus operators in 14
cities (including London). The overall approach for the study in terms of
participation levels was (i) completion of the questionnaire, (ii) follow-
up, (iii) teleconference, and if necessary, (iv) a visit. Table 2 presents
the anonymised list of the cities and the corresponding participation
levels, that received the questionnaires. Note that with the exception of
London, the other cities requested anonymity in the usage of their data.
Two of the operators did not participate, while a third was not able to
participate as it does not have a bus positioning system.
The scale of bus operations in the eleven cities is captured in Table 3
in terms of eet size, passenger kilometres, vehicle kilometres, types of
vehicles in eet (standard, mini/midi, double decker and articulated)
From the responses received, Tables 4 presents the current appli-
cations indicating the number of bus operators that currently employ
them and the level of criticality (high or medium) of automated vehicle
location capability. The level of criticality refers to the impact of lo-
cation accuracy and associated condence (i.e. integrity) on a given
LBS. Hence, mission (e.g. safety) critical services have a high criticality
level.
From the results, the following observations can be made.
Comparing Tables 1 and 4, the current applications from the survey
do not include a number of established ITS applications including
bus lane enforcement, dynamic route guidance, intelligent speed
adaption, collision avoidance, lane control and restraint deploy-
ment.
Of the eighteen current applications, there is no single operator that
covers them all, with the highest number of applications being eight.
Most operators focus on the rst ve services (RTI, service control/
eet management, incident management, network performance and
signal priority).
To facilitate the specication of a location determination system
useful in the long term, the Survey also requested a response on what
were considered by the operators as the future applications. As
expected some of the operators identied some applications that were
already in use by others. Excluding these, the following additional nine
applications were identied by the operators: automatic parking,auto-
matic accident detection,disruption management,intelligent speed assis-
tance,driving range (for electric vehicles),restraint deployment,ticketing
and predictive maintenance. It should be noted that intelligent speed as-
sistance and restraint deployment were identied in the literature review
(Table 1). Another new application is real time performance quality (in-
tegrity) monitoring, identied as missing from the literature review. The
applications from the literature review and the survey are collated in
Table 5 to provide a comprehensive list of location based bus opera-
tions.
In addition to the applications from the literature review, the Eco
Assist service facilitates ecient driving practices to improve fuel
economy and reduce the impacts on the environment. Such practices
are inherently location based. In the case of the monitoring driver be-
haviour service spatio-temporally referenced data (e.g. on speed, ac-
celeration and idling) are captured and used to improve engine e-
ciency, safety and fuel economy. The automatic parking service requires
real-time state estimation aided by spatial information and vehicle
control. It has the potential to both reduce the time taken to conduct
manual parking and accidents in the parking zones.
The automatic accident detection application informs the relevant
stakeholders of the occurrence and location of an accident and its at-
tributes. The information is conveyed to the stakeholders using terres-
trial communication systems. The use of CCTV is mainly for the safety
and security of passengers and drivers. The location and time of the
occurrence of safety and security related incidents and accidents are
vital in their reduction. The other applications are schedule optimisation
(related to headway management), in-depot bus location,driving range
management required for charging of electric vehicles, predictive main-
tenance through automated location based in-use bus inspection and
disruption management enabling the dynamic recovery of operations at a
pre-dened level following dierent kinds of disruption. Furthermore,
the fare and ticketing applications require information on location and
time for the determination of appropriate fares.
The Required Navigation Performance (RNP) performance para-
meters of accuracy, integrity, continuity and availability dened in the
introduction section originated from aviation and are now widely ac-
cepted globally as the performance measures for positioning and na-
vigation systems for most applications including road transport. In road
transport and in particular the provision of ITS services, it is critical that
appropriate techniques are used to derive the performance require-
ments for the determination of bus position, velocity and time (PVT)
from ITS service level requirements. This relationship is captured in
Fig. 1 in which the PVT data are used by the service/application module
to deliver the service(s) to the users.
The requirements derivation process should be technology agnostic
and account for operations/functionality, practical experience, human
factors and mission (e.g. safety) criticality. This could involve the ap-
plication of risk analysis techniques including Hazard and Operability
(HAZOP) and Failure Modes, Eects and Criticality Analysis (FMECA)
which are structured and systematic process for the examination of
operations, process and/or systems to identify and evaluate failures and
Table 2
Bus operator participation levels.
City A B C D E F G H I J K L M N
Questionnaire √√√√ √√√√
Follow-up √√√√ √ √√
Teleconference √√ √ √
Visit √√
Table 3
Scale of bus operations.
Data Items A B C D F H I J K M N
Total number of passengers(Million Passenger-km) 532 409 1026 2592 411 3427 2988 835 1667 1316 8000
Total distance covered (Million Vehicle-km) 43 27 56 111 27 249 180 74 99.0 92.0 534
Total number of vehicles in eet 984 675 989 2141 600 5707 4,656 1332 1,487 1469 9616
Number of Standard vehicles in eet 603 511 0 1,647 477 4872 3768 564 888 1,067 2,704
Number of double-decker vehicles in eet 0 0 986 0 0 0 0 0 334 0 6912
Number of articulated vehicles in eet 304 158 0 494 90 835 677 737 265 247 0
Number of mini/midi vehicles in eet 77 6 3 0 33 0 211 31 0 155 0
M. Elhajj and W.Y. Ochieng Case Studies on Transport Policy 8 (2020) 12–21
16
to quantify their impacts in terms of risk (Gould, 2000). The process
enables performance budgets to be allocated to the dierent compo-
nents of the ITS including the PVT platform. While work is progressing
to quantify the RNP using these approaches, Table 6 presents initial
results based on the consideration of the results of eld experimenta-
tion, operational factors, human factors, level of mission criticality, the
literature and consultation with a Subject Matter Expert (Manela,
2016).
For example, controlled tests undertaken by Transport for London
(Manela, 2016) to quantify the accuracy of GPS using CCTV and ac-
curacy landmarks as a reference, and the subsequent successful delivery
of a number of services have been used here to justify the accuracy
specications for service control, low bridge alarms, headway, and eet
management. Assuming a bus travelling at 15 miles per hour (6.7 m/s)
at a tolerance of 5 s, the along track position accuracy for countdown
(RTI) is specied at 30 m (95%). For some applications such as emer-
gency management where communication with central control is
available, it is assumed that the accuracy could be rened through radio
communication. Furthermore, integrity requirement is driven in this
case by a requirement for an operator to receive a call from a bus within
1.5 s. The availability requirement is specied here based on the data
on radio communication availability of a period of four years, while the
targets for the application is derived from Garage availability over a
period 2.5 years. In the case of bus priority, the targets are based on a
study on bus delay savings undertaken by Transport for London
(Transport for London, 2014). The main driver here is the requirement
that the signal trigger time should not be in error by more than 1 s.
It can be seen that the required positioning accuracy ranges from
1 m to 50 m (95%) while the integrity risk requirement can be as low as
2.10
7
for some services. The required service continuity ranges from
99.5% to 99.9%. The required service availability is from 99.0% to
99.5%. Taking the example of collision avoidance in urban environ-
ments, delivering metre level accuracy with an integrity risk of 2.10
7
,
99.9% continuity of service and 99.5% availability of service, would
present a considerable challenge. The next section proposes both
functional and physical architectures for a positioning/location de-
termination system that has the potential to meet the RNP.
3. Positioning system architecture
3.1. Functional architecture
The current literature addresses the functional and physical archi-
tecture (e.g. Parker, 2008, Hounsell et al., 2012; Tilocca et al., 2017)at
a very high level and does not explicitly account for the impact of ap-
plications, their required navigation performance and operational en-
vironment on the positioning architecture.Specically, they do not
delve into the details of integrity or quality monitoring, a key re-
quirement for a mission critical operation such as bus service provision.
In addition, there is no detail on the sensor integration concepts which
Table 4
Current applications - survey.
Application/Service Operator (City) Criticality
High-H
Medium-M
Total
ABCDFHI JKMN
Travel Time/Real Time Passenger Information (RTI) √√ √√ √√ √√√ √ H11
Service Control/Fleet Management √ √√ √ √√ √√√ √ H11
Incident Management √√√√√√H8
Network Performance √√ √√√√ H8
Trac Signal Priority √√√ √ √√ H8
Ticketing System √√√H3
Fare System √√M2
Low Bridge Alarms √√M2
Audit of Compliance by the Transport Authority M1
Automatic Passenger Counting M1
Control Depot Leaving Times M1
Detection of Trac Jam Hotspots M1
Eco Assist M1
Headway M1
In-Depot Bus Location M1
Monitoring Driver Behaviour M1
Schedule Optimisation M1
Closed Circuit TV (CCTV) M1
Total 7 5 7 5 4 7 7 5 5 3 8 M
Table 5
Current and future LBS for urban bus operations.
Urban Bus operation LBS Stakeholders
Travel Time/Real time passenger
information
Bus riders, travel and trac
management and operators.
Service control/eet management Operators
Incident management Operators
Network performance Operators, regulators
Trac Signal (Bus) Priority Travel and trac management and
operators.
Ticketing system Riders, operators
Fare system Riders, operators
Low bridge alarms Riders, drivers, trac management and
operators
Audit of compliance by the transport
authority
Regulators
Automatic passenger counting Drivers, operators
Control depot leaving times Drivers, operators
Detection of trac jam hotspots Drivers, operators
Eco assist Operators
Headway Operators
In-depot bus location Operators
Monitoring driver behaviour Operators
Schedule optimisation Operators
CCTV Drivers, operators
Bus lane enforcement Drivers, operators
Dynamic route guidance Drivers, operators
Intelligent speed adaptation Drivers, operators
Collision avoidance Drivers, riders, operators
Lane control Drivers, operators
Restraint deployment Drivers, passengers, operators
Automatic parking Drivers
Automatic accident detection Drivers, riders, operators
Disruption management Drivers, operators
Driving range (for electric vehicles) Drivers, operators
Real time performance (integrity)
monitoring
Drivers, operators
Predictive maintenance Operators
M. Elhajj and W.Y. Ochieng Case Studies on Transport Policy 8 (2020) 12–21
17
are important for maximising the availability of bus location determi-
nation systems. Therefore, based on the LBS and their requirements
determined in section 2, and the characteristics of the operational en-
vironment, Fig. 2 presents a functional architecture for the bus posi-
tioning system. It accounts for the need for integrity monitoring of the
positioning/navigation solution to support the mission critical appli-
cations. Furthermore, as bus positioning in built environments pose
particular challenges in terms of attenuation and blockage of signals,
the architecture is designed to improve the continuity and availability
of positioning. Traditionally, this has been done by combining position
solutions from dierent sensors and then generating a nal position
solution, referred to as position domain integration (Elhajj, 2017).
However, position domain integration is undesirable since for ex-
ample, a GPS position solution is required before integration. In this
case, when there are less than four satellites in good geometry for 4-D
positioning, there would be no position x from the GPS terminal re-
lying instead on dead reckoning. This is exacerbated by the need for
integrity monitoring, where measurement redundancy is required.
Hence, position domain integration requires that the individual sensors
generate enough measurements for positioning prior to integration
(Groves, 2013). To address this, it is sensible to perform the integration
at the measurements level, i.e. measurement domain integration, in
which the measurements from the dierent sensors are combined or
integrated to generate a position solution. The advantage of measure-
ment domain integration over position domain integration is that it
facilitates positioning when the individual sensors do not have an
adequate number of measurements.
The features above are captured in Fig. 2, in which the core sensor
data capture function represents GNSS, while the augmentation data
capture function represents alternative complementary systems such as
dead reckoning or opportunistic sensors. The measurement domain
integration function integrates the measurements from the core and
augmentation sensors, and uses positioning aiding information (e.g.
distance and direction) from the map matching function to generate
position solutions together with the data required for integrity (quality)
monitoring. The solutions that pass the integrity monitoring function
tests are output as the nal position of the bus. In the event that a
hazardous measurement error is detected, the integrity monitoring
function identies it and passes the information back to the measure-
ment domain integration function for a new position solution excluding
the signicantly erroneous measurement.
3.2. Physical architecture
There are a wide variety of technologies that could in principle be
used to support the positioning/navigation module of bus operation
systems including space based (GNSS and their augmentations), ter-
restrial systems (Dead Reckoning - DR, Wireless Local Area
NetworksWLAN and Map Matching-MM) and space/based terrestrial
augmentations (Elhajj, 2017). Taking into account the strengths and
weaknesses of these technologies (e.g. GNSS high accuracy but low
availability in the urban environment; DR low accuracy but high
availability; WLAN low to high accuracy and increasing prevalence of
nodes/access points/transmitters), the complementary strengths are
used to propose an architecture for urban bus operations (Fig. 3)to
implement the functions in Fig. 2. The core of the system is multi-
constellation and multi-frequency GNSS (metre level accuracy), stra-
tegically augmented with terrestrial signals of opportunity (SOOP), in
particular decimeter level Wi-Fi based positioning (Nur et al., 2010; Nur
et al., 2013). In addition DR using the odometer (displacement mea-
surement, Δd) and low cost rate gyroscopes (change in direction, Δθ),
and in some cases aided by map derived data, can be useful in the event
that there are failures with either the GNSS or opportunistic signals.
The multi-sensor fusion is undertaken in the measurement domain
to maximize availability of the positioning solution in terms of the
Easting and Northing coordinates (
E
N,
)
, velocity (
v)
heading (θ)and
their uncertainties (
σ
σσ,,
Nvθ
). Advanced map-matching algorithms
(Quddus et al., 2007) should be used for link identication (LinkID) and
physical location determination (
E
Ns,,
)
LINK LINK mm . Integrity mon-
itoring is performed both within the data fusion and map-matching
functions. Note that there is a wide variety of integration algorithms
such as basic and weighted averaging, consensus sensing, weighted
least squares, kalman ltering (and its variations including its non-
linear version referred to as the extended kalman lter), neural
Fig. 1. Relationship between service level and PVT performance.
M. Elhajj and W.Y. Ochieng Case Studies on Transport Policy 8 (2020) 12–21
18
Table 6
Required navigation performance for urban bus LBS operation.
Application/Service User group Along track Accuracy
(m, 95%)
Cross track Accuracy
(m, 95%)
Integrity Continuity (%) Availability (%) Update Rate (UR)
Alarm Limit
(m)
Time-To-Alert
(TTA) (s)
Integrity Risk
Service control Operators 12 15.00 20/25 < UR 1/round trip (100 min) 99.5 99.0 30 s or event
Countdown - RTI Riders, travel/trac managers,
operators
30 15.00 50/25 < UR 1/round trip (100 min) 99.5 99.0 30 s or event
Bus priority Travel/trac managers and
operators
6 15.00 10/25 2.0 1/round trip (100 min) 99.5 99.0 1 s
Low bridge alarms Riders, drivers, trac
managers, operators
3 1.25 5/2 1.0 1/10 years (bus) 1/year
(network)
99.5 99.0 1 s
Headway Operators, trac managers 10 15.00 18/25 < UR 10
5
99.5 99.0 30 s or event
Bus Lane enforcement Trac enforcement 12 1.40 2.45 1 (bus) or < UR 2.10
7
99.9 99.5 1 s (bus) or 30 s
(operator)
Route guidance Bus drivers, bus operators 10 12.00 18/20 2.0 10
4
99.9 99.5 1 s
Application/Service User group Along track Accuracy (m,
95%)
Cross track Accuracy (m,
95%)
Integrity Continuity Availability Update Rate (UR)
Alarm Limit
(m)
Time-To-Alert
(TTA) (s)
Integrity Risk
Fleet management Bus operators and managers 10 12.00 18/20 < UR 1/round trip (100
min)
99.9% 99.5 30 s or event
Intelligent Speed Assistance Bus driver, bus operators
/managers
10 1.40 20/2.45 2.0 2.10
7
99.9% 99.5 1 s (bus) or 30s
(operator)
Collision avoidance Riders, operators, managers, asset
owners
1 1.40 1.5/2.45 0.1 2.10
7
99.9% 99.5 1 s (bus)
Emergency management Operators, managers, eet asset
owners
50 1.40 80/2.45 1.0 103106 99.9% 99.5 Event trigger
Lane Control Bus drivers and operators 12 1.40 2.45 1.0 2.10
7
99.9% 99.5 1 s (bus) or 30s
(operator)
Restraint Deployment Bus drivers and operators 1 1.80 1.5/3 < UR 10
5
99.9% 99.5 1 s (bus) or 30s
(operator)
M. Elhajj and W.Y. Ochieng Case Studies on Transport Policy 8 (2020) 12–21
19
networks, fuzzy logic and particle ltering (Groves, 2013).
Although signicant research eort has been dedicated to aspects of
the architecture in Fig. 3 (Elhajj, 2017), there are many challenges that
are still to be overcome. These challenges can be attributed to the RNP.
In terms of accuracy, achieving metre level accuracy requires: (i) the
use of the more precise but higher complexity ambiguous GNSS ob-
servable, the carrier phase, (ii) the adoption of multiple-frequency
GNSS positioning capability exploiting advanced modernised and new
signals, (iii) decimetre level positioning accuracy with SOOP with the
potential to replace DR, and (iv) high resolution maps and advanced
map-matching algorithms. In terms of integrity, multi-sensor integrity
monitoring capabilities are still to be developed particularly for high
accuracy positioning. On continuity and availability, intelligent context
adaptive multi-sensor integration techniques are required to eectively
realise positioning ubiquity in the urban environment. Finally, appro-
priate testing and validation techniques are required for very low per-
centile risk requirements, to support approvals and certication pro-
cesses.
4. Conclusions
The advantages of buses over private-vehicles dominated urban
transport systems include less congestion, lower energy consumption
and less emissions. Buses therefore, have the potential to be the most
Fig. 2. Bus positioning functional architecture.
Fig. 3. Proposed architecture for urban bus operations.
M. Elhajj and W.Y. Ochieng Case Studies on Transport Policy 8 (2020) 12–21
20
universal solution for sustainable urban travel from an economic, en-
vironmental and social aspects. This has spurred a speedy evolution in
bus technology, infrastructure, concepts of operation, business models
and operational best practice. In addition, signicant eort is being
expended on increasing the attractiveness and awareness of bus sys-
tems. A detailed literature review has been undertaken to capture the
state-of-the-art in these aspects of bus operations (including actual
implementation, and research and development). While the current
initiatives including specic examples in Europe, have the potential to
improve bus operations, none addresses in an exhaustive way, the
signicant operational element that relies on Location Based Services
(LBS). This paper complements these initiative to increase operational
eciency recognising that this depends to a signicant extent on the
knowledge of the position and location of buses within the transport
network. It is argued that neither the services nor their required navi-
gation performance have been determined exhaustively and agreed
universally. Hence, a comprehensive list of the location based services
for urban bus operations have been determined and presented in this
paper. The methodology adopted included a review of the relevant
literature and a dedicated representative global survey of the main bus
operators in eleven major cities. Furthermore, a process to quantify the
Required Navigation Performance (RNP) for the services has been dis-
cussed at a high level and the initial results presented. Based on the
services and the initial RNP, a plausible positioning systems functional
and physical architectures have been proposed, with distinctive fea-
tures that address both the complexity of the urban bus operational
environment and the level of criticality of the location based services.
Research is ongoing to quantify the RNP and assess the impact of the
proposed architectures on the RNP. The applications, their require-
ments and positioning system architecture are essential for the relevant
stakeholders for the formulation of appropriate policies, regulation,
service provision, and development and procurement of urban bus
positioning systems.
Acknowledgements
The authors acknowledge the assistance given by Dr Shaojun Feng
from the Centre for Transport Studies (CTS) and Dr Manela Mauro from
Transport for London (TfL) on the quantication of the required navi-
gation performance. The Transport Strategy Centre (TSC) within the
CTS is acknowledged also for the help with execution of the survey of
bus operator.
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