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ITM Challenges and Approaches

This article discusses challenges facing traffic management systems (TMS) in smart cities due to increasing population and vehicle traffic. It reviews technologies used in TMS to address issues like traffic congestion, accidents, and pollution. The authors analyze two types of congestion and their economic impacts. They also discuss security threats to TMS and recent projects aiming to advance smart transportation. Open challenges are presented along with a vision for developing robust future TMS in smart cities.

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ITM Challenges and Approaches

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Shubham Shekhar

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This article has been accepted for publication in a future issue of this journal, but has not been

fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/COMST.2014.2339817, IEEE Communications Surveys & Tutorials
MANUSCRIPT SUBMITTED TO IEEE COMMUNICATIONS SURVEYS & TUTORIALS

A Communications-oriented Perspective on Traffic

Management Systems for Smart Cities: Challenges
and Innovative Approaches
Soufiene Djahel, Ronan Doolan, Gabriel-Miro Muntean and John Murphy

Abstract—The growing size of cities and increasing population a tremendous growth. One major consequence of this in-
mobility have determined a rapid increase in the number of crease is related to management problems that range from
vehicles on the roads, which has resulted in many challenges traffic congestion control to driving safety and environmental
for road traffic management authorities in relation to traffic
congestion, accidents and air pollution. Over the recent years, impact. Over recent years, researchers from both industry
researchers from both industry and academia were focusing their and academia were focusing their efforts on leveraging the
efforts on exploiting the advances in sensing, communication advances in wireless sensing equipment and communication
and dynamic adaptive technologies to make the existing road technologies, along with simulation and modeling tools to
Traffic Management Systems (TMS) more efficient to cope with make the existing road TMS more efficient, enabling them to
the above issues in future smart cities. However, these efforts
are still insufficient to build a reliable and secure TMS that cope with the above issues in future smart cities. One of the
can handle the foreseeable rise of population and vehicles in most critical consequence of traffic congestion is the delay of
smart cities. In this survey, we present an up to date review of emergency services, such as police, fire and rescue operations,
the different technologies used in the different phases involved medical services, etc. Indeed, very often individual human
in a TMS, and discuss the potential use of smart cars and lives, general population safety and institutional economic or
social media to enable fast and more accurate traffic congestion
detection and mitigation. We also provide a thorough study financial situation in case of incidents, robberies or criminal
of the security threats that may jeopardize the efficiency of attacks highly depend on the efficiency and timely response
the TMS and endanger drivers’ lives. Furthermore, the most of emergency vehicle services. Additionally, recent road traffic
significant and recent European and worldwide projects dealing statistics reveal another extremely serious concern which is the
with traffic congestion issues are briefly discussed to highlight increasing number of vehicle crashes. These crashes usually
their contribution to the advancement of smart transportation.
Finally, we discuss some open challenges and present our own happen in the areas around congested roads as the drivers tend
vision to develop robust TMSs for future smart cities. to drive faster, before or after encountering congestions, in
order to compensate for the experienced delay. The negative
Index Terms—Traffic Management System (TMS), Smart
Cities, Smart Transportation, Data Sensing and Gathering, consequences of these accidents are many, at personal, group
VANETs, Route Planning, Traffic prediction. and societal levels, and could be exacerbated if emergency
vehicles are involved in a crash.
I. I NTRODUCTION However, most large cities in the world are still suffering
MART cities is a label that is associated with a significant from traffic congestion, despite employing different solutions
S paradigm shift of interest towards proposing and using
various innovative technologies to make cities ”smarter” in
to reduce it, including using TMSs deploying advanced con-
gestion control mechanisms. In order to best contribute to
order to improve the people’s quality of life. As a very impor- the ongoing efforts to solve the traffic congestion problem
tant and highly visible initiative, the European Commission or at least reduce its impact, there is a need to understand
has launched the European Initiative on Smart Cities in 2010 the different types of congestion and their impact. Two major
[1] that addresses four dimensions of the city: buildings, types of congestion can be distinguished: recurrent and non-
heating and cooling systems, electricity and transport. Strictly recurrent. Recurrent congestion usually occurs when a large
related to transportation, the goal is to identify and support number of vehicles use the limited space of the road network
sustainable forms of transportation, to build intelligent public simultaneously (e.g. weekday morning and afternoon peak
transportation systems based on real-time information, Traffic hours). Non-recurrent congestion mainly results from random
Management Systems (TMS) for congestion avoidance, safety events such as traffic incidents (e.g. car crash or a stalled
and green applications (e.g. to reduce fuel consumption, gas vehicle), work zones, bad weather conditions and some special
emissions or energy consumption). events like sport events, Christmas, etc. According to recent
In this context, it is worth noting that the number of statistics (http://www.transport2012.org), road traffic conges-
cars using the limited road network infrastructure has seen tion costs billions to the world economy. For instance losses
have reached:
Soufiene Djahel and John Murphy are with Performance Engineering
Laboratory, University College Dublin, Ireland. • 200 e billions in Europe (2% of GDP)
Ronan Doolan and Gabriel-Miro Muntean are with Performance Engi- • $101 billion in USA
neering Laboratory, Dublin City University, Ireland.
Manuscript received November 2013. Aggregate delays of 4.8 billion hours were experienced

1553-877X (c) 2013 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See
http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI
10.1109/COMST.2014.2339817, IEEE Communications Surveys & Tutorials
MANUSCRIPT SUBMITTED TO IEEE COMMUNICATIONS SURVEYS & TUTORIALS

and 1.9 billion gallons of fuel were wasted worldwide. These address the Data Sensing and Gathering phase with a brief
statistics give a clear indication of the devastating impact that description of the different technologies used for road traffic
congestion has on individuals, companies (e.g. freight and and events monitoring, and discuss alternative technologies
transport companies, etc.) and society. that may improve the quality and accuracy of the collected
Unfortunately, to date the existing TMSs do not provide data. Afterwards, we discuss Data Fusion, Processing and Ag-
sufficient and accurate road traffic information to enable gregation techniques, followed by a description of the services
granular and timely monitoring and management of the road that a TMS may provide based on the collected and fused
traffic network. Some of the reasons include: lack of granular data, including short term traffic prediction information, route
data collection, inability to meaningfully aggregate much of planning and parking management information, in sections
the data collected, and a lack of complex management systems IV and V, respectively. In section VI, we investigate the
capable of providing accurate views of the road transport different routing approaches used in VANETs to exchange
network. This inability to effectively monitor and manage the collected road traffic information among the vehicles, the
the traffic maintains traffic congestion high, which in turn beacon congestion problem in IEEE 802.11p as well as the
affects road safety (i.e. increases the number of death on simulation tools used for traffic and VANET-based application
the roads), augments fuel consumption and causes large gas simulation. Subsequently, we show how smart vehicles may
emissions. The main solutions used by the existing TMSs significantly improve the efficiency of current TMSs, in section
to manage the traffic after an incident or during peak hours VII. In section VIII, we discuss the different threats that may
is changing/adapting traffic lights cycles, closing road lanes jeopardize the security and privacy of TMSs. In section IX, we
and intersections, etc. These solutions have limited efficiency present the major international projects aiming at improving
when the increasing number of cars are using the limited the different aspects of future TMSs. In the final sections, our
road infrastructure and constantly new solutions to be used vision on open challenges is discussed and this survey paper
by TMSs are being proposed by the research community. is concluded.
This survey paper provides a comprehensive study of the
solutions employed by existing TMSs, by looking at the
different phases of a modern TMS in a smart city environment,
II. OVERVIEW OF F UTURE T RAFFIC M ANAGEMENT
from information gathering to service delivery. In particular
S YSTEMS
the paper discusses the Data Sensing and Gathering (DSG)
phase in which heterogeneous road monitoring equipment A Traffic Management System (TMS) offers capabilities
measure traffic parameters (such as traffic volume, speed and that can potentially be used to reduce road traffic congestion,
road segments occupancy, etc.), and periodically report these improve response time to incidents, and ensure a better travel
readings to a management entity. These monitoring tools can experience for commuters. A typical TMS consists of a set
detect random incidents and immediately report them through of complementary phases, as shown in Figure 1, each of
broadband wireless networks, cellular networks or mobile which plays a specific role in ensuring efficient monitoring
sensing applications. As these data feeds are fused and aggre- and control of the traffic flow in the city. The cornerstone
gated during the Data Fusion, Processing and Aggregation phase of a TMS is Data Sensing and Gathering (DSG)
(DFPA) phase to extract useful traffic information, the paper in which heterogeneous road monitoring equipment measure
analyses this phase in detail. The Data Exploitation (DE) traffic parameters (such as traffic volumes, speed and road
phase uses the acquired knowledge from the data processing segments occupancy, etc.), and periodically report these read-
phase to compute optimal routes for the vehicles, short-term ings to a central entity. For example, these monitoring tools
traffic forecasts, and various other road traffic statistics. Finally can detect random incidents and immediately report them
in the Service Delivery (SD) phase, the TMS delivers this through wireless networks, cellular networks or mobile sensing
knowledge to the end users (such as drivers, authorities, applications. Subsequently, these data feeds are fused and ag-
private companies, etc.) using a variety of devices such as gregated during the Data Fusion, Processing and Aggregation
smart phones, vehicle on-board units, etc. Moreover, the paper (DFPA) phase to extract useful traffic information. The next
investigates the advantages of using alternative approaches, phase, Data Exploitation (DE), uses this acquired knowledge
such as mobile sensing and social media, to improve TMS’s from the processed data to compute: optimal routes for the
efficiency and accuracy. This survey also discusses the security vehicles, short term traffic forecasts, and various other road
attacks that may threaten the integrity of traffic data, leading traffic statistics. Finally in the Service Delivery (SD) phase,
to non-optimal and incorrect decisions taken by the TMS in the TMS delivers this knowledge to the end users (such as
relation to the detected/reported incidents. Furthermore, the drivers, authorities, private companies, etc.) using a variety of
most significant and recent projects trying to address traffic devices such as smart phones, vehicles’ on-board units, etc.
congestion are briefly discussed, highlighting their contribu- The capabilities offered by a TMS are not confined to serve
tion to the advancement of TMS. Finally, open challenges are drivers and road authorities only, but can also contribute signif-
noted and the authors’ vision on robust TMS development for icantly to the economic growth of a country, to the preservation
future smart cities is presented. of citizens’ safety and to the support of national security. The
The remainder of this paper is organized as follows. In the currently deployed technologies for road traffic surveillance
next section, we give an overview of future TMSs, highlighting still suffer from a lack of traffic parameters measurement
their important conceptual phases and design stages. Then, we accuracy and real-time report of events that occur on the roads,

Figure 1: Data life cycle in smart transportation

especially in developing countries. Moreover, the gathered incidents on the roads.

traffic data usually needs to undergo a filtering process to An adequate TMS for future smart cities should fulfill the
improve its quality and eliminate the noise. Deploying highly following requirements:
sophisticated equipment to ensure accurate estimation of traffic • Ensure higher accuracy in estimating traffic conditions
flows and timely detection of emergency events may not be and better efficiency in dealing with emergency situations
the ideal solution, due to the limitation in financial resources on the roads, compared to the existing TMSs.
to support dense deployment and the maintenance of such • Be able to efficiently manage the traffic in road networks
equipment, in addition to their lack of flexibility. Therefore, of varying size and characteristics.
alternative cost-effective and flexible solutions are needed to • Provide real-time road traffic simulation and visualisation
guarantee better management of road traffic in both developed to help authorities more efficiently manage the road
and developing countries. infrastructure and improve route planning for commuters.
• Ensure simplified and smooth integration of existing
A modern TMS aims to overcome some of the above systems and new technologies, and manage the evolution
limitations by designing innovative approaches able to exploit of these systems.
advanced technologies to efficiently monitor the evolving crit- A high level architectural overview of a modern TMS is
ical road infrastructure. These approaches should be scalable depicted in Figure 2. This figure shows the main components
enough in order to enable better control of the traffic flow of the TMS needed to deliver the collected road traffic infor-
and enhanced management of large cities’ road networks. This mation to the intended end consumers (e.g. road authorities,
will certainly improve the accuracy of the acquired real-time Police, drivers etc). As we can see from this figure, the core
traffic information and the short-term traffic prediction. This system of the TMS collects road traffic information from
will enable making and using short-term predictions based on heterogeneous data sources according to the consumer needs
current traffic volumes to identify bottlenecks and make more and specific requests. These data feeds are then aggregated
informed decisions about how to best reroute traffic, change and stored in an unified format in one or multiple databases.
lane priorities, modify traffic light sequences, etc. A modern Later, upon reception of a consumer request, the core system
TMS should also provide a visual tool that can display in processes the request and extracts the pertinent data from
real-time traffic information related to location of bottlenecks, the appropriate database. Then the requested information is
incidents, and congestion level in each road segment, as well sent back to the intended consumer, tailored for their specific
as estimated travel time from one location to another in purposes: e.g. analysis and statistics, decision-making, etc.
the road network. In this way, the transport authorities will
have an overall view of the road network in real-time, and III. T RAFFIC DATA SENSING AND GATHERING
will enable the best support for improvements in the traffic Data sensing and gathering phase focuses on the scalable
flow management and more efficient reactions to emergency collection of traffic flow information from a large number of

Figure 2: A simple architecture of a modern TMS

heterogeneous sources. Many of the current deployed systems able to collect data from a specific region of interest under
used by traffic management agencies collect data in a variety specific time constraints, while minimising cost and spectrum
of formats, time scales, and granularity. This is due to the usage and maximising system utilisation.
fact that those systems have been deployed at different time
periods with little or no integration between them. This creates A. Wireless Sensor Networks (WSNs)
a management problem for operators whom must manage,
Due to their high efficiency and accuracy in sensing the
analyse and interpret all of this dissimilar data. A modern
different events, wireless sensors have been widely deployed
TMS will analyse a number of the existing traffic informa-
in various environments for data collection and monitoring
tion collection mechanisms employed by city authorities and
purposes [76], [77]. Indeed, it is foreseen that WSNs can
identify where new technologies and systems can be used to
enable several applications that may significantly improve the
improve the accuracy, timeliness and cost efficiency of data
control of road traffic flow and ease its management, examples
collection. In addition, these new data collection technologies
of these applications are the real-time control of traffic lights
must provide a more informed explanation of the root causes
[73] and their adaptation according to the congestion level
behind the increasing congestion levels on the roads. More
[74], as well as parking spaces management [72]. However,
specifically, the current trends in TMS development consist in
the deployment of wireless sensors in the road environment to
leveraging advanced communication and sensing technologies
realize these applications face several challenges, in addition
like Wireless Sensor Networks (WSNs), cellular networks,
to the well-known issues in WSNs [75], that require careful
mobile sensing and social media feeds as potential solutions
consideration and design of appropriate protocols. Among
to circumvent the limitation of the existing systems.
these challenges, we highlight the need of a fast and reliable
The main wireless technology used for events sensing and MAC access protocol [31] and data forwarding mechanisms
gathering on the roads is the tiny sensor devices. These sensors to guarantee timely transmission of critical messages carrying
could be mounted on vehicles, at the roadside or under the information about the occurred emergency events on the road.
road pavement to sense and report different events. In the An example of WSNs deployment for road traffic monitoring
former case, the in-vehicles embedded sensors monitor and is shown in Figure 3.
measure several parameters related to the vehicle operations It is also worth mentioning that the expected wide and dense
and communicate them to the nearby vehicles or roadside deployment of wireless sensors on the roads necessitates the
units. In the latter cases, the sensors are mainly used to design of robust data aggregation techniques to deal with the
measure the passing vehicles’ speed, the traffic volumes as high redundancy and correlation of the transmitted informa-
well other environmental parameters. WSNs can be used to tion, especially from neighboring sensors, as the redundant
interconnect these sensors and greatly reduce the cost of transmission of this information may lead to quick depletion
monitoring systems deployment. In an urban scenario, we can of sensors battery, increase the delay of emergency messages,
imagine a plethora of sensors being deployed to collect data as well as the collision rate. To reduce traffic data redundancy,
about traffic conditions, air pollution, environmental noise and the optimal placement of wireless sensors on road networks
many other applications. Information can also be obtained should be investigated and a trade-off solution between the
from vehicles that have proper sensors and communication number of sensors deployed in a specific area, and road events
antennas on board; these would primarily be public transporta- detection and accuracy should be designed. The spatial and
tion vehicles, taxis, police cars, and freight vehicles. A modern temporal correlation of traffic data are intrinsic characteristics
TMS will, therefore, focus on designing innovative solutions of road networks, which can be leveraged to solve both sensor

data aggregation and optimal sensors placement problems in reporting of incidents and accurate travel time estimation for
future smart cities. improving commuters travel experience. The key enabler of
the widespread of mobile sensing applications, mainly for
B. Machine to Machine (M2M) communication traffic monitoring purposes, is the voluntary participation of
the users. These users demand high level of privacy, anonymity
A key technology that is a promising solution for reliable
and security guarantees in order to participate to such a system.
and fast traffic data monitoring and collection is Machine to
Indeed, these requirements constitute major concerns that need
Machine (M2M) communication. The M2M technology has
to be carefully addressed to instigate larger participation of
recently attracted increasing attention from both academic and
mobile devices users to mobile sensing applications. These
industrial researchers aiming to foster its application for data
issues can be dealt with as discussed in the following to
collection in various environments. Recent forecasts [116],
mitigate their impact on the TMS efficiency and accuracy of
[117] indicate an outstanding market growth over the next few
its decisions.
years for M2M devices usage and connectivity. According to
• Trust management of mobile sensing data sources: how
these forecasts, billions of devices will be potentially able to
benefit from the M2M technology. The report published by to build a trust relationship with the mobile sensing data
the Organisation for Economic Co-operation and Development source? In this case, reputation systems, such as [144],
(OECD) in [118] reveals that around 5 billion mobile wireless need to be used to continually assess the level of trust-
devices are currently connected to mobile wireless sensor worthiness of each mobile sensing data source. A mobile
networks, and foresees that this number will grow to reach data source is deemed trustworthy if the information it
50 billion connected devices by the end of the decade. In has reported has been validated by either other mobile
M2M communication, a sensor gathers traffic data and sends sources or a trusted data source such as road-side sensors,
it via wireless communication/cellular/3G/LTE networks to- induction loops or CCTV cameras.
• Privacy preservation of mobile devices users: several
wards one or multiple central servers for processing purposes.
The ability of M2M devices to avoid the multi-hop trans- levels of privacy could be defined in the context of
mission, as opposed to WSNs, makes the data transmission smart cities, and users can adjust the setting of their
faster and more reliable, which represents a significant benefit devices to increase/decrease the privacy level according,
for the sensors reporting delay critical events. Moreover, it for example, to traffic conditions (e.g. normal driving con-
is foreseeable that this technology will significantly enhance ditions, traffic jam, incident ) and the service they need
the accuracy of data collection and lead to more flexible to request from the TMS (e.g. optimal/fastest route to
deployment of sensors on the roads. their destination). Therefore, adaptive privacy protection
M2M over LTE networks is expected to be a key aspect techniques that manage the users privacy preferences and
of future TMS. These M2M devices are equipped with access adapt the privacy level to the contextual factors in smart
technology capable of communicating in a reliable, fast and cities are required.
• Design robust authentication techniques to prevent any
extremely efficient way with the central entity that processes
and aggregates the collected data. Moreover, M2M solutions misuse of the system such as identity spoofing and fake
support different classes of QoS, thus they can efficiently alerts, etc.
collect prioritized data from multiple sources and ensure
that appropriate QoS is applied to each stream. The M2M D. Social media
technology provides an extremely attractive solution for data
In the context of smart cities, social media feeds, such as
collection in urban areas due to its management benefits in
Twitter and Facebook for instance, can play an important
terms of reduced data reporting delay, high efficiency, and
role in improving the accuracy and richness of the traffic
low complexity. However, deploying M2M devices as an
information provided by the traditional monitoring equipment
alternative of WSNs technology will incur an additional cost
such as road sensors and induction loops. Despite the fact that
related to the use of cellular/3G/LTE networks. Therefore,
these pieces of equipment can measure the vehicles’ speed and
this may hinder the wide deployment of M2M technology
road segments’ occupancy to enable the estimation of traffic
by city traffic managers, especially for cities with limited
congestion level, they are unable to identify the root event
financial resources, which is the case of the majority of cities
that has led to this situation. [70] has shown that relying
in developing countries.
on social media feeds, in addition to the traditional data
sources, can significantly enrich the real-time perception of
C. Mobile sensing traffic conditions in the cities, and help to explain the reasons
In addition to the above data sources, mobile sensing behind the variation of the congestion level. Indeed, revealing
using mobile devices is expected to enable fast detection the real causes of the sudden increase of the congestion level
of events on the roads and enhance the accuracy of traffic (e.g. accident, road works, political or social protest etc) will
conditions monitoring. According to recent studies in [32] enable more appropriate reaction from the road authorities
and [33], mobile crowd-sensing systems have been recently to alleviate the impact of this situation. Therefore, there is
used to provide more accurate real-time traffic information on a need to deeply investigate [71] this traffic data source to
a large scale, using smart phones that enable services such enhance citizens’ quality of life and aid the traffic authorities
as, accurate localization of vehicles, faster and more precise for efficient management of the increasing number of cars.

Figure 3: Scenario illustrating wireless technology deployment in road environment for data sensing and gathering

In order to maximize the benefits of using this novel traffic of data and the content accuracy. At least an indication of the
data source, we need to raise the citizens awareness to its level of trust in the data is required to be present in order for
utility. Applying a reward system, for example, to encourage any further processing to make use of it in an informed manner.
the citizens to use social networks to report accidents and un- To this end, some recent efforts have been devoted to design
usual events that occur in the roads is highly recommended. In robust security and privacy preservation solutions. In [138],
this case, any citizen who reports an authentic emergency/non the authors have investigated the various hacking techniques
emergency event will get a reward which will increase their that may threaten the reliability of such data sources, and
rank among road users. Higher ranked users could get higher presented potential mitigation methods. This paper highlights
quality of service from the TMS. For example, when they the dangers incurred by poor security, such as identity theft
sign up to the TMS to plan a trip they will get the route and corporate espionage etc, and proposes novel architecture
that satisfies all their requirements, while other drivers may to mainly improve the security of personal data. On the other
just get a route that satisfies a subset of their preferences. hand, [139] has shown the potential security and privacy
Using social media feeds may also assist the road authorities challenges that may arise as a consequence of the emergence
for better planning of road networks expansion, as well as of MMSN (Multimedia-oriented Mobile Social Network) con-
optimal road signs placement and speed limits setting. This is cept. MMSN is a new social media application in which users
feasible by analyzing the citizens’ feedback, including drivers in the vicinity share useful multimedia content of interest, such
and pedestrians, which may significantly improve traffic flow as road traffic information etc. However, this application may
control and improve road safety. create new security threats such as privacy disclosure. The
authors presented three MMSN applications emphasising their
However, at the same time, there is a need to verify the
corresponding security and privacy problems, and discussed a
accuracy of the data acquired from such poorly reliable sources
set of solutions to face those threats. The studied applications
of information. Mechanisms are needed to be proposed and
were mainly content query, service evaluation, and content
deployed which best balance the need for fast information
filtering. In addition to the above works, other researchers
propagation with validation and verification of both the source

have also thoroughly investigated privacy concerns and trust describes the data concepts for traffic data, metadata, network
management issues in social networks such as [148], [149] devices and events. Moreover, IBM Intelligent transportation
and [147]. product uses TMDD standard to ease interfacing with Traffic
Management Centers and Advanced Traffic Management Sys-
tems (ATMS). The aim of the TMDD standard is to provide
IV. DATA F USION , P ROCESSING AND AGGREGATION
a standards-based, high-level definition in a protocol indepen-
T ECHNIQUES
dent manner, with which a system specification interface can
Although there are a large number of systems currently be prepared. Besides its main purpose, which is supporting
employed for road traffic monitoring, there is very little traffic management applications, all ITS practical areas can
integration between these systems and in most cases the data benefit from TMDD format such as for emergency situation
from each system has different types, formats and metadata. management, products shipment and travel information for
Much of the data is also of different time scales and levels communication needs.
of granularity. During the DFPA phase of TMS numerous The ultimate objective of the introduction of TMDD stan-
techniques are applied to combine these heterogeneous data dard and the development of data fusion and integration
sources to produce unified metrics that can be processed and techniques is to simplify and automate data collection from
delivered to various consumers based on their requirements. existing and future systems, and reduce data aggregation and
A modern TMS should enable the real-time aggregation of conversion delay and complexity in order to improve the
these high volume data sets from a plethora of heterogeneous overall system efficiency. To this end, recent research studies
sources. It will also store this data over long periods of time have designed innovative techniques to ensure efficient fusion
to perform statistical analysis, which can be further used to and integration of the traffic data gathered from heterogeneous
better plan and deploy changes/upgrades to the transportation road monitoring equipment. A snapshot of these recent works
network. This will enable the system to combine the various is given below.
traffic measurements produced by existing traffic systems - The authors of [99] have developed a technique to improve
such as induction loop counters, CCTV cameras and cellular the quality of detector data which is combined with Floating
handover information - to monitor and manage traffic flow car data (FCD). It is acknowledged that discovering the
within the city. dynamic properties of the traffic is a difficult task due to
The main steps involved in DFPA phase are summarized in the sparseness of induction loops and low penetration rates
Figure 4 which describes the processing flow of the data and of vehicles transmitting FCD. To overcome this issue, the
what are the different issues that this phase deals with. After authors have used conventional spatio-temporal interpolation
receiving the gathered data, the DFPA engine applies cleansing to determine fine structures, such as stop and go waves from
and verification techniques to identify incorrect, inaccurate the collected data. Moreover, interpolation can also be used to
and incomplete data and either correct or remove them. compensate for detector failure. The efficiency of this approach
Afterwards, these data will be prepared to the fusion phase has been evaluated using real traffic dataset collected from the
by resolving time synchronization issues and exploiting the roads of Birmingham which are known by the high penetration
geographical correlation of these data to further reduce their rate of induction loops.
amount or extract new knowledge. Subsequently, the chosen A more recent technique has been proposed in [100], where
fusion algorithm is thus applied to integrate the different set ASDA (Automatische Staudynamikanalyse: Automatic Track-
of data into a consistent, accurate and valuable representation ing of Moving Jams)/FOTO (Forecasting of traffic objects)
of the road network traffic. The output of this phase will model has been applied to process induction loops data, and
be then transmitted to the core TMS system and samples of then fuse the resulting data with FCD. Both of these datasets
the forwarded data will be stored for future aggregation and are processed to determine traffic state changes (e.g. from
redundancy removal purposes. free flow to congested flow, stopped to congested flow etc).
In order to enable the TMS to scale to larger cities, the de- Subsequently, they are fused to construct a spatiotemporal
ployed techniques must be capable of aggregating traffic data map of the traffic state changes. This work has shown that
feeds from various levels and at various levels of granularity. a probe vehicles penetration rate of 1.5 % has yielded a very
For example, a modern TMS will investigate how traffic feeds similar model to detectors deployed at every 1-2km, hence the
can be aggregated and filtered for specific geographic regions assertion is that now they can be easily combined, resulting in
before being passed to the core system. This reduces the a more efficient traffic model than the original ASDA /FOTO
amount of information processing and filtering that is required model.
at the core, and will allow the system to scale and evolve over Fuzzy rough set theory has been also used in [101] to
time and to be deployed to cover increasingly large geographic fuse heterogeneous traffic data feeds, each of which can often
regions. yield contradictory evidence. By using this theory, a significant
Due to the heterogeneity of the collected traffic data for- reduction of the redundant data can be achieved. In addition,
mats, a common data format is required to enable high level a novel fusion technique based on Yagers formula [105] has
management and processing of the aggregated data. IBM been developed to rank the different data sources. Furthermore,
intelligent transportation product, for example, uses the Traffic the maximum fuzzy probability function is applied for the
Management Data Dictionary (TMDD) standard developed by different datasets to avoid the subjective factor effect. It is
the Institute of Transportation Engineers (ITE). This standard worthwhile to notice that this work is different from the two

to whether the forecasting model will be implemented as part

of a TMS or a Traveller Information System (TIS), and the
area where it will be used (e.g. highway, urban arterials etc).
The second factor concerns data resolution which is highly
dependent on the chosen forecasting horizon and step. The
horizon refers to the extent of time ahead for which the traffic
conditions will be predicted, while the step defines the time
interval upon which the prediction is made, as stated in [102].
The forecasting accuracy is strongly related to the choice of
the horizon and step values, hence defining appropriate time
interval for both of them is compulsory to achieve accurate
prediction. According to the experiments conducted in [103]
the prediction accuracy is inversely proportional to the forecast
horizon duration. The Highway Capacity manual (2000), as
well as some studies in the literature, have suggested 15 min
as the most appropriate horizon value. For the step value, the
most used value is 5 min interval due to the high variability
of the traffic flow. However, we argue that the horizon and
step values should be also adapted to the requirements of
the application for which the forecasting algorithm will be
used. Finally, the third factor that affects the forecasting
accuracy is the technique used to model the traffic data, such
as statistical and time series analysis models [108] [109],
the well-known Auto-Regressive Integrated Moving Average
(ARIMA) model [104] [107], Neural Networks [106], pattern
recognition techniques, etc. In addition, some recent works
Figure 4: The different steps in DFPA phase have used spatio-temporal correlation of traffic flow across
the road network [58], as well as the collected GPS data [61]
to provide more precise prediction.
previous techniques as it considers induction loops, video Accurate real-time road traffic forecasting is a required ca-
detectors and OD analysers instead of induction loops and pability to avail of advanced smart transportation technologies.
floating car data. The efficiency of the developed fusion From the point of view of transport authorities, the ability to
technique is tested against traffic data from Hangzhou city in predict traffic pattern evolution is a key requirement to enable
China, and the obtained results have proven its effectiveness. efficient management of the traffic flow in urban, sub-urban
and highway scenarios. Traffic prediction can enable the early
V. TMS SERVICES identification of traffic jams, which allows the traffic authori-
ties to take preventive measures to alleviate the congestion on
As shown in Figure 1, after fusing and aggregating the
the roads. On the other hand, traffic prediction is a substantial
collected data, the TMS can exploit it to provide various
step towards providing accurate journey duration estimation
services. These services are mainly vehicles routing to shorten
for the commuters, as it is one of the major inputs of route
the commuter journey, traffic prediction that enables early
planning algorithms, as highlighted in section V-B.
detection of bottlenecks and more informed decisions to face
In the last decade, numerous traffic prediction techniques
these issues, parking management systems that ensure optimal
have been proposed in the literature; however, most of them
usage of the available spots and interact with routing and
have been devoted to highways rather than highly congested
prediction services for improved control of traffic flow, and
urban arterials, which are far more likely to be monitored by
finally infotainment services that provide useful information
the traffic authorities. Therefore, the design of accurate and
(e.g. tourism information, multimedia contents delivery over
scalable traffic prediction tools for urban road networks is
VANTEs, shopping centers offers, cinema, ...) for both drivers
required. To achieve this goal, the efforts should be focused on
and passengers. In the following, we will discuss the im-
defining real-time forward looking analysis techniques that use
portance of these services and how their efficiency can be
the large stores of historical traffic data, the social media feeds
improved to optimize the traffic in the transportation system.
as well as real-time traffic feeds (i.e. traffic flow, road segments
occupancy and speed) to predict how traffic conditions will
A. Short-term traffic prediction evolve in time-frames ranging from the next few minutes to a
Traffic forecasting research topic has been widely investi- couple of hours, as illustrated in Figure 5. It is worth noting
gated in several academic studies aiming at outlining the key that social media feeds are substantial input as they provide
factors that influence its design and modelling decisions. In a comprehensive explanation of the traffic conditions, which
general, the following three factors characterize a short-term significantly helps the traffic managers to take the adequate
traffic forecasting system [102]. First, its scope which refers actions. The key principle of a prediction algorithm is to

Figure 5: Overview of traffic prediction system and its impact on TMS efficiency

use a combination of simulation, traffic modelling, real-time concerns that may arise as consequence of the sensitivity of
feeds and historical data to predict how the traffic situation some used data feeds such as social media and GPS data are
will evolve in the near future. These techniques may also also covered.
leverage some properties of the road network such as the Designing effective tools for fast, scalable and accurate
spatio-temporal correlation for faster inference of traffic jam, road traffic prediction is a key solution to overcome the
as well as other techniques as discussed above. The typical weaknesses of the existing TMSs. The fast prediction allows
outputs of a prediction algorithm are the traffic forecast and the the traffic managers to take early actions to control the traffic
identification of the bottlenecks. Moreover, it can also explore load and prevent the congestion state. Fast and accurate road
a set of what-if scenarios through simulation to infer the traffic prediction is a paramount technique to enable better
impact of random incidents on the expected traffic conditions, efficiency of TMSs and mitigate the awful impact of road
and therefore more informed decisions will be taken in case of traffic congestion. However, most of prediction algorithms are
real incident. These decisions may involve adjusting the traffic likely to combine historical data with real-time traffic feeds,
signal timing, the message signs as well as closing some road and apply some advanced and complex modelling approaches
lanes or changing the driving rules. to predict the future traffic state, as discussed earlier in this
A comprehensive comparison of the major traffic prediction section. Therefore, the legacy simulation approaches are not
approaches in the literature is provided in Table III. Those suitable in this case and distributed simulation is required
approaches are compared based on their achieved prediction to allow fast and accurate reaction to the change in traffic
accuracy, their scalability level when applied to large scale congestion in order to mitigate its consequences. The main
road networks, the modelling technique used (i.e. parametric or advantages of fast road traffic simulation are summarized
non-parametric), the road environment in which the forecast- below:
ing approach is applied (i.e. highway or urban area). Moreover,
we also considered the type of traffic data source, meaning • Enable more accurate recommendations from the TMS to
whether the prediction is based on data collected from fixed police men regulating traffic at a junction, especially after
monitoring equipment, such as sensors and CCTV cameras, an incident or during special events. Indeed, after an ac-
or using mobile data sources such as floating GPS data and cident it is a hard task for a human to take the optimal ac-
SMS, social data feeds etc. This metric is very important tion that mitigates other problems (i.e. accidents, increase
as the heterogeneity of data sources and the variety of their the congestion, block other roads etc). Hence, adequate
format and level of granularity may add extra constraints on recommendations to traffic authorities, for example in the
the designed prediction algorithm, and may also affect its effi- case of an accident what are the optimal lanes to close
ciency and accuracy. Since some prediction techniques impose to ease traffic congestion?, are needed. This would be
some constraints on the quality, type and format of the used based upon exploring the entire solution space (i.e. what-
data feeds in order to ensure high level of accuracy we have if scenarios) to achieve a reasonably optimal solution.
also addressed this metric. Finally, the privacy and security Therefore, this requires extremely fast simulation tools to

provide the optimal recommendation within a very short finds the fastest and most reliable route with less computation
time-frame. complexity is, therefore, required. The reliability of the route
• Enable faster and more efficient emergency service deliv- in this context refers to the probability that no abnormal delay
ery (i.e. ambulance, police and fire fighters cars) which occurs on any link constructing the fastest route during the
significantly reduces the incurred financial loss and save vehicle journey, as stated in [97].
human lives. Here, fast simulation allows the traffic In addition to the travel time, other criteria such as, the
authorities to detect the traffic bottlenecks in advance and length of the best route, its cost and its associated level of
take effective actions to prevent them. driving easiness and risk are considered by some drivers due
• Enable better load balancing of the traffic over the road to their specific needs. The cost of the route is computed
networks infrastructure, which decreases the traffic con- in terms of the fuel consumption level and the number of
gestion and its economic and environmental impact as toll tags included in this route. The fuel consumption is
well as improve road safety. highly dependent on the traffic conditions as well as the road
conditions measured in terms of the roughness and the gradient
of the road segments of the chosen route [98]. The easiness
B. Route planning
of driving varies according to the number of turns, number of
The growing complexity of the big cities’ road networks traffic lights, lanes width and number of hills in the best route,
has led to an unprecedented expansion in the automotive and it could be an interesting criterion for elderly, new drivers
navigation systems market. These systems, such as TOMTOM and people with poor driving skills. Finally, the level of risk
[130] and GARMIN [131], have made the journey of drivers of a route is calculated based on historical statistics about the
easier and more comfortable due to the valuable information number and severity of accidents happened on a given route,
that they provide like the city roads map, GPS localisation and some drivers may prefer to avoid this route for safety
and the guided route towards the destination. Despite the purposes.
popularity of these systems, fast and accurate route search Although several dynamic routing algorithms have been
algorithms under the rapid and sudden variation of traffic proposed such as, [87] [88], and [89], many problems are
conditions are still required to accommodate the needs of still unresolved yet. A noteworthy problem in this context is
future smart and autonomous cars. As opposed to static routing how can we ensure better usage of the road infrastructure
algorithms used for shortest path finding in graph theory, while maintaining a reasonable satisfaction of the drivers
route planning algorithms must update the best route assigned preferences? Load balancing mechanisms based on centralized
to each vehicle as soon as any change in road and traffic system architecture are more appropriate in this case, but guar-
conditions that affect at least one road segment that this vehicle anteeing their efficiency is another challenge, especially during
should pass through is detected [30], [145], [146]. the peak hours. We foresee, then, that managing efficiently
A typical dynamic route planning algorithm for smart cars is the growing number of vehicles in smart cities necessitates a
described in Figure 6. This figure emphasizes the main inputs mix of centralized and distributed system architectures through
of a dynamic routing algorithm, its output and the road events leveraging vehicular communication and mobile sensing infor-
that may trigger an update of this output. These inputs consist mation during the decision making process. For example, the
of the city road network modelled as a directed graph in order vehicle can combine the alternative route received from the
to reflect one and two ways road segments, the vehicle features system with the acquired information from the vehicles ahead
(e.g. its height, weight, type ), current traffic conditions and the to take more information decision about the alternative route
short term traffic forecasts, as well as the driver preferences. that it will follow. Moreover, the system can adapt the quality
By applying the routing algorithm on the directed graph and of the best route assigned to each vehicle according to the
taking into account all the other inputs, the best route is level of participation of the driver to mobile sensing process as
returned. This latter should be updated dynamically, during the well as the level of information disclosure. Consequently, this
vehicles journey upon occurrence of any event that may lead can help to achieve a balance of the traffic load and maintain
to the failure of a road segment included in this route. Notice adaptive satisfaction of the drivers. To get more insight into
that the failure of a road segment means its closure due to an the proposed approaches in the literature to improve route
incident or road works, or the abnormal increase of travel delay planning in smart cities, the reader may refer to the following
across it. Updating the best route means quickly providing an recent papers [43], [44], [45], [46], [47] and [48].
alternative route that mitigates the detected bottlenecks. One of
the challenges here is how to keep the quality of the alternative
route very close to that of the failed best route? C. Parking Management Systems
Usually, the best route depends on driver preferences which Another important service that results from data exploitation
may include one criterion or a combination of several criteria. is parking management which is foreseen to play a key role in
The travel time is the preferred criterion for most of the improving traffic congestion control and reducing its impact.
drivers due to the critical consequences of the delay. For To be more specific, an advanced parking management system
example, people may lose their job for recurrent late arrival at should be operating in tight cooperation with the prediction
work, companies may lose money for late delivery of goods and routing components of a TMS due to the fact that knowing
to their customers and injured people may lose their lives the volume of traffic heading towards a destination will give
due to the delay of emergency services. An algorithm that more insights about the expected demands on parking spots

Table I: Comparison of existing traffic prediction approaches

Accuracy Scalability Parametric Application Data source Fixed-location Privacy & security

level environment constraints data source issues

ARIMA Low Medium Yes Highway High Yes Low

Neural Networks Low Medium No Highway High Yes Low

Statistical models Medium High Yes Urban area Medium No Medium

GPS data High High No Urban area High No High

based techniques

Figure 6: Route planning algorithms: main inputs and functioning

in the near future. Therefore, the routing component may parking spots. The major consequences of this problem are
adapt the individual vehicles routes based on its awareness time wasted, increased cost for the journeys and especially,
of the available parking spots in an urban area, such that the increase of the congestion level, as the drivers will
the traffic jam is mitigated and the usage of parking spots occupy the limited road infrastructure for longer time than
is optimized. Figure 7 illustrates a scenario in which the was expected. Therefore, developing efficient solutions for
parking management system regularly reports the available parking management and smart phone based applications (e.g.
parking spots to the routing component in order to increase its ParkYa [133] application developed in Ireland and parkinglook
awareness of parking availability. Then, the routing component [134] in Australia) that signpost parking locations and provide
combines this information with the traffic forecasts reported real-time information about spot availability to drivers will
by the prediction component, and adapts the routing decisions certainly alleviate the traffic load on the roads and enhance
accordingly, in order to achieve a global traffic load balance the TMS effectiveness. In order to contribute to the ongoing
and maximize the usage of available parking spaces. To this efforts aiming at making smart cities happen, worldsensing
end, the routing component may request the parking manage- [135] has developed a green and self-sustainable smart parking
ment system to adjust the number of free spots to be advertised solution named Fastprk which makes use of M2M technology
through its mobile applications in accordance with the routing to ensure real-time monitoring of available parking spaces.
objectives to direct the drivers, for example, towards a specific Fastprk has proven its efficiency through the success achieved
parking in a given area such that the occurrence of traffic jam in the city of Moscow, known by its heavy traffic congestion,
is mitigated. where Worldsensing has deployed a huge number of parking
Nowadays, finding an available parking spot is becoming a monitoring sensors (approximately 15,000) to provide both
difficult problem for car drivers. Usually long time is spent end users and city council authority with real-time information
looking for available parking places, especially in big cities. regarding parking space occupancy. This solution allows the
Often taking public transportation rather than driving own users to find their parking places via electronic street signs
cars is the preferred option for many people. This problem or smart phone applications. Fastprk was shown to reduce
is mainly due to the lack of efficient parking management travel time and fuel consumption, by reducing the time and
systems that ensure early notification of the drivers about the distance driven to find a parking space. In addition to these
available spots as well as the limited number of available applications, other solutions are being investigated by the

research community such as sophisticated carpooling [136], Szczurek et al. [56] have proposed a machine learning
[137] and public transportation systems that may stimulate the algorithm for determining whether a given car park will have
citizens to use these alternative transportation modes, instead a space to park. In this system, when a vehicle leaves a
of driving their own cars. parking space, it sends a message over VANETs announcing
Most recently, many researchers have designed solutions to that a parking space has become available and specifies its
detect available parking spaces and share this information with corresponding coordinates. This work has shown a reduction
other cars, via V2V communication, within a specific area. in the time spent searching for a car park space of over 25%
Mathur et al. [49] have focused on urban on street-parking compared to a blind search.
availability and designed a mobile system named ParkNet It is well known that on street parking offers most car park-
that uses vehicles equipped with GPS receiver along with ing spaces in cities, which means that an efficient management
ultrasonic sensors to determine the parking spots occupancy of these spaces may lead to a substantial benefits for both
while passing by. Based on real data collected in San Fran- city and citizens. Unlike off-street parking lots where the car
cisco, ParkNet has proven an accuracy of more than 90% in park gate can be used as a sensor to assess the occupancy
determining the free parking spots. It would also achieve a level, a sensor per parking space is required to monitor and
cost saving of an estimated factor of 10 compared to static detect the availability of on street parking, which represents
sensors deployed at each street-parking place. a significant cost for their deployment. However, to reduce
Klappenecker et al. [50] have proposed a system to predict this cost, Evenepoel et al.[57] have proposed to deploy the
the number of available parking spaces when a vehicle reaches sensors on a fraction of on-street car park spaces only and
the parking. In this system, the parking ticket machine regu- then use extrapolation to infer the amount of cars parked in
larly communicates the number of available spaces to the ve- the entire city. A probabilistic model was devised to quantify
hicles upon arrival using Markov chain based estimation. This the reliability and efficiency of the proposed approach and the
system, however, doesn’t exploit the free spaces efficiently as obtained results were promising, as they show that ensuring
more than one vehicle may drive to the same parking spot as slightly less than 2 % of parking space coverage by sensors
described in [53]. To overcome this drawback, [53] proposes a would be optimal. Therefore, a significant reduction of the
reservation protocol that allows a vehicle to claim a spot when sensor deployment cost would be achieved. However, the main
it becomes free, thus an optimal use of the available spots is shortcoming of using so few sensors is that some drivers
guaranteed in this case. might be tempted to ”cheat” in order to guarantee easy and
Panayappan et al. [51] have proposed to deploy sensors on fast parking for themselves or their colleagues at work. For
the sides of each vehicle to detect the presence of any vehicle example, an employee may intentionally park on the parking
in the place next to it. This is a useful mechanism to prevent spot equipped with a sensor so that the road would appear full
abuse as the multiple cars and car park sensors will check to other users, whereas this is not the case.
whether the space is free. In the paper by Kokolaki et al. [52],
each vehicle gathers the location of each empty parking space VI. V EHICULAR N ETWORKING S UPPORT FOR DATA
and then forwards this over the ad-hoc network. This approach G ATHERING AND S ERVICE D ELIVERY
was compared with a non-assisted search and centralized In this section, we explore the different routing approaches
server approach. The VANETs based scheme didn’t always used in vehicular networks to disseminate the gathered data
outperform the centralized server but the paper highlights the among the vehicles as well as the information transmitted by
fact that the VANETs based scheme requires no additional the TMS or other service providers towards all the vehicles
infrastructure to be built, so it is a much more cost effective or a sub-set of them, discuss their advantages and disadvan-
solution. tages, and provide a comprehensive comparison of their main
A decentralized and scalable parking spots information features. We also address the challenging problem of beacon
system has been developed in [55] to inform the drivers congestion control in IEEE 802.11p MAC layer and briefly
about parking spots availability in an urban area. This system describe the pioneer works that have dealt with this issue.
makes use of VANETs to disseminate micro and macro park- Moreover, we outline the recent advances in simulation tools
ing information either locally or at large scale, respectively. for road traffic and VANETs based applications, and highlight
Micro information refers to free parking spots coordinated their main features and degree of realism.
by one automat while macro (i.e. aggregated) information
covers several parking within one urban area. This system
has shown high efficiency under realistic model of German A. Vehicular Ad-hoc Networks (VANETs)
city in which 5% of the vehicles, out of 10000, are equipped Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure
with wireless communication capabilities. To complement the (V2I) communications are expected to play a key role in the
previous work, Caliskan et al. [54] have developed a model development of TMS in smart cities. The efficiency of this
using homogenous Markov chains and queueing theory that type of communication depends on the reliability of the WAVE
estimates the future occupancy of parking spots located within system and mainly on IEEE 802.11p MAC protocol [40], in
the vehicle’s destination area at its arrival time. Based on the addition to the information dissemination (i.e. routing) pro-
parking information received through VANETs the vehicles tocols. In this section, we will present, classify and compare
apply this model to decide about its orientation to one of the the most significant protocols proposed to find the best route
available parking. for the exchanged packets among non-neighboring vehicles

Figure 7: Illustration of Prediction, Routing and Parking services cooperation

in road network. Usually, these protocols use either the road the network infrastructure. Other nodes then set up a virtual
network map, the vehicles mobility model, both of them or location by judging their distance from those sink nodes. For
none of them to accurately determine an end to end connected efficient routing, a greedy geographic approach is used to route
route between the source and destination vehicles. Therefore, the exchanged messages among the nodes, and the evalua-
we classify these protocols, as shown in Figure 8, into four tion results have shown that this scheme outperforms GPSR
categories based on their awareness of these two parameters (Greedy perimeter Stateless Routing) in terms of energy-
(i.e. the map and mobility model), as described below. efficiency, path length and robustness.
1) Context-unaware routing protocols: these routing
In Location-Based Routing Algorithm with Cluster-Based
mechanisms do not take into account the road map and nor
Flooding (LORA CBF) [4], the cluster based flooding mech-
the predicted mobility of vehicles.
anism is used, where a number of gateways are chosen for
Greedy Perimeter Coordinator Routing (GPCR) [3] is a inter clusters communication, in addition to the cluster head.
position based routing protocol. It uses the fact that streets When a data packet needs to be sent, the sender first checks
and junctions form a natural planar graph. In this protocol, its routing table to find the location of the destination node.
messages are forwarded along the street with decisions only If this location is missing then a location request message is
taken at junctions. GPCR uses a repair strategy to get rid of broadcasted to the network. Upon reception of this request,
local minimums. This protocol does not require a static streets the destination node or any intermediate node, which has
map as it can heuristically detect the junctions on the road, fresh location information of the destination node, sends a
however it is not resilient to network partitioning that may location reply message to the source. The data packet is then
occur due to links loss. transmitted through this route. The hierarchical architecture
In the 3rule routing protocol [86], a set of sink nodes of LORA CBF leads to shorter route discovery time but the
aware of their location are deployed and configured to form overhead increases considerably [41].

Figure 8: A classification of VANETs routing protocols based on their awareness of the mobility model and the road map

DV-CAST [5] applies two different approaches according Other protocols in this category are Urban Multi-Hop
to the network connectivity, it uses the broadcast suppression Broadcast protocol(UMB) [8] which addresses the broadcast
technique in order to reduce the broadcast overhead in case storm and hidden nodes problem and ARBR [11] which uses
of a dense network, while a store-carry and forward method carry and forward scheme to overcome network fragmentation
is used in a sparse network. The network density level is issue.
determined based on the size of one-hop neighbors list. This 3) Mobility model-aware routing protocols: the following
protocol overcomes some of the previous protocols’ limitations routing mechanisms leverage the knowledge of vehicles’ mo-
as it reduces the broadcast storm and adapts its routing bility models for messages routing purposes.
approach to deal with network disconnection problem. Connectivity-Aware Routing (CAR) [12] uses a greedy
Broadcomm [13] is a fast routing protocol specifically forwarding approach with anchor points to find the route
designed for safety applications. It divides the highway into relaying origin-destination pairs. In CAR, the messages are
virtual cells which move along at the average highway speed. forwarded to the closest node to the next anchor point instead
At the center of each virtual cell, some nodes are designated of the closest node to the destination, and the location of
as cell reflectors which, in turn, act as virtual base stations. It this latter is tracked so that the route can be adjusted to
is worth mentioning that cell reflectors are similar to cluster provide connectivity even if the destination has moved a great
heads, except that several cell reflectors may co-exist within deal. Notice that the incorporation of CAR routing approach
one cell. The main weakness of this protocol is the high in GPSR has shown an improvement of the performance
incurred overhead. by 30%, however the main shortcoming of this protocol is
2) Map-aware routing protocols: in this category of rout- its inefficiency to handle different sub-paths under frequent
ing protocols, map information is a cornerstone for calculating topology changes.
the end to end path for a data packet. Predictive Directional Greedy Routing protocol (PDGR)
SADV [6] is an infrastructure based routing protocol that [14] routes the vehicles based on both their current and
presumes the existence of a static node (i.e. a Road-side Unit predicted positions. It applies a greedy strategy and forwards
(RSU)) at each junction. Each RSU has a digital street map the messages in the direction of the destination vehicle without
to determine which road presents the best trajectory. A data a predetermined route. PDGR considers both the position
packet waits at the RSU till a route to the next intersection and movement of a vehicle for forwarding decisions. It has
is established. This route is selected based on the delay been shown that PDGR outperforms GPSR in terms of delay,
estimation of each road in order to achieve a near optimal delivery ratio and overhead.
choice. SADV improves the packet delivery ratio and presents MUlti-hop Routing for Urban VANET (MURU) [18] is
an enabler for RSUs placement in road networks. another protocol that attempts to increase packet delivery ratio

through early detection of the broken links in the VANETs, Therefore, an ideal routing protocol for VANETs in the context
whereas Vector-based TRAcking DEtection (V-TRADE) [16] of smart cities should be aware of extra parameters, in addition
uses vehicles positions and directions to ensure more efficient to the mobility model and map, such as the shape of the roads,
routing. destination of other vehicles, channel interference level, etc.
4) Map and Mobility mode-aware routing protocols: This In the Table II, we compare the different routing ap-
class of protocols exploits both the road map and vehicles proaches presented above according to the following criteria:
mobility model to ensure a robust route for messages delivery the incurred communication and computation overhead, to
among vehicles. what extent the protocol is scalable?, the end-to-end delay
VADD (Vehicle-Assisted Data Delivery) [7] is designed of the transmitted packets, its efficiency in terms of packet
specifically to route data packets in sparse VANETs with delivery ratio, its resiliency to VANET fragmentation due to
frequent network fragmentation due to the high mobility of the high mobility of vehicles, whether its applicable in urban
vehicles. These packets will be transmitted over the routes with or highway scenarios or both of them, and finally whether it
shortest transmission delay. In case of network fragmentation, requires the help of the road-side infrastructure or not?.
the packet is forwarded to a vehicle that crosses the network In addition to the routing function, the media service in
partitions first and then forwards it towards the destination. VANETs has become a hot topic in recent years and several
VADD determines whether there is a direct route to the contributions have been proposed to enhance the efficiency
destination by analyzing the map of the area and the traffic of VANET applications and services. In [140], the authors
conditions around it. This protocol ensures higher delivery have addressed VANET-based entertainment services such as
ratio compared with GPSR [17] and DSR [2], as stated in [7]. video streaming, file sharing, mobile office and gaming etc.
However, a large delay may occur under varying topologies As noted in the paper, these services can make the drivers and
and vehicles density. passengers travel experience more pleasant, however a number
Inter-Vehicles Geocast (IVG) [21] is a safety based protocol of research challenges need to be overcome to make those
that broadcasts an alarm message to all the vehicles in a services efficient and robust. To this end, several challenges
given area if there is a danger. The vehicles are in the danger have been highlighted such as frequent network disconnection,
area if this danger is in front of them. In this case, these high mobility of vehicles etc. the authors discuss also the
vehicles constitute the multicast group that will receive the requirements that the existing channel access and resources
alarm message, then forward it in the backward direction. In management schemes in VANETs need to satisfy in order to
order to reduce the gratuitous alarm messages IVG takes into be suitable to support entertainment and safety applications.
account the braking distance before broadcasting this message. Another work [141] has focused on Video on Demand (VoD)
However, if the danger is immediate the alarm message is sent, services provided to vehicles using P2P networks. This work
regardless of this distance, to prevent crashes. proposes Quality oriented User centric VoD (QUVoD) de-
GVGrid [23] uses a reactive routing approach to construct signed specifically for vehicular networks. QUVoD introduces
a route from a fixed source node to another vehicle located a new grouping based storage strategy as well as a novel
within a specific geographic area. GVGrid divides the road speculation-based prefetching strategy. The simulation results
network map into a set of uniform squares and assume that of this work have proven its superior performance benefit in
each vehicle is equipped with a digital map and is aware comparison with the state of art solutions.
of its location and direction through GPS. This protocol
ensures route recovery in case of link break due to vehicles
mobility. GVGrid uses stop signs and highways with constant B. IEEE 802.11p congestion control
vehicles distance as prediction indicators for vehicles mobility Congestion control is probably the most challenging issue at
to enhance messages routing. MAC layer in VANETs [79] given the fact that IEEE 802.11 is
In addition to the above protocols, A-STAR [19] and Gy- well known by its scalability problem. The research commu-
TAR [22] have been also proposed in this category. A-STAR is nity has highlighted the importance of congestion control in
an Anchor-based Street and Traffic Aware routing protocol that VANETs, and the ETSI ITS framework [80], [81] has defined
combines street map and bus routes information to determine a set of mechanisms to deal with this issue such as data rate
the fastest path that exhibits higher connectivity. A-stars use of adaptation, transmission power control and beacons frequency
the right hand rule is inefficiently biased in one direction, as adjustment. However, these mechanisms are still inefficient
stated in [20]. The latter protocol, GyTAR, employs a greedy due to the characteristics of the control channel (CCH) [40] in
strategy that takes into account real-time traffic conditions and vehicular environment as well as the large number of vehicles
road topology, in addition to a recovery strategy to overcome unusually contend for channel access.
local optimum. Most of the proposed solutions to control the congestion in
To summarize, each of these routing protocol categories has VANETs focus on adjusting the transmission power [35], [82]
advantages and shortcomings and might be suitable for some used for broadcasting the beacons to prevent the congestion
road traffic scenarios and not applicable in others. Moreover, state or at least reduce its impact on the performance. However,
the awareness of vehicle mobility models and road network in some situations, this may cause an isolation of some
maps might not be sufficient to meet the requirements of safety vehicles when vehicles density decreases. This is because of
applications, especially in emergency scenarios where both fast the highly fluctuating topology of VANETs as the vehicles
and reliable dissemination of danger alerts are compulsory. move very fast and change their directions often. To take into

account this specific feature of VANETs, [83] has proposed to discreet-event network simulators, such as ns-2, ns-3, OP-
assign data rates based on the average utility of the messages NET, OMNet++ and QualNet, have been widely used by
transmitted by each vehicle. Thereby, vehicles transmitting the researchers to validate their ideas and approaches, they
information with a high utility (e.g. safety messages) for the cannot be used in ITS scenarios without an accurate vehicular
VANET are allowed to consume a larger part of the available mobility model. Moreover, if the ITS application influences
bandwidth. This scheme requires that the vehicles share the the behaviour/mobility of the vehicles then a real-time bidi-
information that allows to each of them to calculate its own rectional coupling of network and road traffic simulators is
data rate. Hence, the overhead incurred by the exchanged required [78]. To this end, some European research projects
messages may significantly reduce the available bandwidth, have recently developed platforms integrating both network
especially when the number of vehicles gets larger. and microscopic traffic simulators to improve the accuracy and
Data rate control [84] has been also proposed to deal realism of ITS solutions evaluation. The most known platforms
with MAC layer congestion in the ETSI ITS framework. are Veins [93] which is based on OMNeT++ and SUMO, and
The idea behind this adaptive mechanism is that a higher iTETRIS [91] that integrates NS-3 with SUMO.
data rate implies the message occupies the channel for a There are two major avenues for road traffic and IVC model-
smaller duration, thus allowing more transmissions to take ing and simulation. One approach, taken by macro simulators,
place. An important observation here is that the higher the considers the overall traffic flow modeling and simulation
data rate provided by a modulation, the higher the signal-to- on the road network, and no detailed level information and
interference ratio (SIR) required at the receiver side in order related input, output or processing (e.g. at vehicle level)
to correctly decode the message. Simulation studies (e.g. [84]) are being considered. The second avenue is taken by micro
have shown that the reception probability for geographically simulators, which simulate individual vehicles in the traffic
close vehicles is hardly affected and, in these conditions, systems. Vehicles are seen as important actors in the road
adjusting the data rate gives similar results with transmission network system and not only are mobile in this context, but
power control. Therefore, choosing the modulation based on also generate, process and sink network data traffic. In order to
the local vehicles density seems to be a promising solution in best address the current research and development needs, this
crowded environments like VANETs. section focuses on micro traffic simulators. A study conducted
Other works, such as [85] and [29], have proposed to in- between 2009 and 2011 on top level international conference
crease the bandwidth available for the CCH to reduce/mitigate papers [92] has identified the three most popular road traffic
the impact of congestion. In [29], the authors have proposed micro simulators used by the research and development com-
a cognitive radio technology based technique that allows the munity. The Simulation of Urban Mobility (SUMO) has been
vehicles to opportunistically use the detected holes in the reported as used by more than 20 % of the papers with a peak
primary users frequency spectrum in their neighbourhood. This of 30 % in 2010. The use of SUMO is almost constant, trend
extra bandwidth gained by the vehicles could be mainly used which continues today. In contrast, the dedicated vehicular
to ensure rapid transmission of alert messages in emergency network movement simulator VanetMobiSim, which has been
cases. Radio cognitive technology has been also applied in used in nearly 20 % of the publications surveyed in 2009,
the following works [37], [38], [42] and [39] to improve the has experienced a marginal use lately. VISSIM, which is a
reliability of safety applications in VANETs. In addition to commercial tool, maintained an average proportion of about
the above discussed works, the reader may refer to [27], [25], 6% during last three years. In meanwhile other micro traffic
[26], [34], [24], [24], and [36] to get a broader idea about the simulators have also been proposed and are being used by the
different solutions designed to mitigate the congestion problem research and development community focusing on vehicular
in VANETs. traffic modeling and simulations. Next most important of these
solutions are presented.
C. Current trends in road traffic and VANETs simulation In what follows, we will briefly discuss the most used
In June 2013, researchers from the Transportation Research road traffic simulation tools and applications in the research
Institute of University of Michigan have showcased V2V and community, and highlight their main features and limitations,
V2I communication demo [132], during which the vehicles as shown in Table III.
equipped with IEEE 802.11p communication technology were 1) SUMO: SUMO is an open-source traffic micro-simulator
able to exchange their position, speed and direction with designed to handle large road networks. Sumo was mainly
similarly equipped peers as well as with the roadside infras- developed by the Institute of Transport Systems at the German
tructure like traffic lights and tollbooths. This unprecedented Aerospace Center. SUMO allows for space-continuous and
real world vehicular communication experiment has involved time-discrete vehicle movement modeling and simulations.
2 800 vehicles of different types and shown that vehicular Some of the features include: different vehicle types, multi-
communication technology can play a key role for improving lane streets with lane changing support, different right-of-way
roads safety. Despite that, simulation remains one of the rules, traffic lights, etc. SUMO provides network-wide, edge-
strategic tools for evaluating the performance of the developed based, vehicle-based, and detector-based outputs. It has a fast
VANETs communication protocols and ITS applications due openGL graphical user interface, scales very well (i.e. tens
to the inaccessibility or the high cost of the resources needed of thousands of streets) and provides fast execution speed
(e.g. vehicles equipped with communication capabilities, road- (e.g. 100.000 vehicle updates/s on a 1GHz machine). It also
side units etc) to carry out real world tests. Although many supports interoperability with other applications at run-time.

Table II: Comparison of the main characteristics of the surveyed vehicular routing protocols

Characteristics

Communication Computation Scalability Latency Delivery Network partitioning Target Infrastructure

overhead overhead level ratio resiliency scenario dependant

GPCR [3] Low Low Medium High Low No Urban No

3rule [86] Low Low Unknown Unknown High No All Yes

LORA-CBF [4] Medium Low High Low High Medium Urban No

DV-CAST [5] Low Low High Low Medium Very High All No

SADV [6] Low Low Medium Medium Medium High Urban Yes

VADD [7] Low Medium Medium Medium Low High Rural No

UMB [8] Medium Medium Medium High Medium Medium Urban Yes

ARBR [11] Low Medium Medium Medium High High Urban Yes

CAR [12] Medium Medium Medium Medium Medium Medium All Yes

BROADCOMM [13] High Low Medium Low Low Medium Highway Yes

V-TRADE [16] Medium Low Medium Medium Low No Highway No

PDGR [14] Medium Medium Medium Medium Medium No Urban No

MURU [18] Low Medium Medium Low Medium High Urban No

A-star [19] Medium Low Medium Medium Low Medium Urban No

IVG [21] Low Low High Low Medium High Highway No

GyTAR [22] Low Low Medium Low Medium High Urban No

GVGrid [23] Medium Medium Medium Medium Medium Medium Urban Yes

Sumo allows the user to import different sources such as the position of vehicles is calculated and updated regularly.
VISSIM and open street map. Sumo is coded in C++ [90]. VISSIM offers a high level of complexity in terms of display
2) iTETRIS: iTETRIS has opted for integrating two well- with both 2D and 3D views. VISSIM can be used to investigate
known and widely used open source simulation platforms. private and public transport including in particular pedestrian
SUMO (http://sumo.sourceforge.net) as an open-source mi- movements scenarios. The use of VISSIM is moderate with
croscopic traffic platform and Network Simulator 3 - NS3 roughly 6,000 individual PTV Vissim Licenses around the
(http://www.nsnam.org/) for wireless communications mod- world. There has also been roughly 3,800 downloads of the
eling and simulations. The capability to perform large-scale VISSIM demo in 2012 [95].
simulations and to support multi-radio/technology nodes was Additionally there are several applications which allow for
a key-parameter for the selection. iTETRIS resulted to have vehicular traffic modeling, simulations and analysis. Among
the best performance in terms of scalability [65]. these for instance SIDRA TRIP allows to compare travel
3) STRAW: STRAW (STreet RAndom Waypoint) is an conditions on alternative routes, to assess network traffic
open-source traffic simulator built by researchers at the performance, and to analyse vehicle movements and traffic
AquaLab at the Northwestern University, US. STRAW runs performance. It is based on collected GPS data inputted by
on top of the highly efficient JiST/SWANS discrete-event the user to form traffic traces, which are then used during
network simulator. STRAW includes a realistic mobility model simulation and analysis. SIDRA has quite a small user base
with very good level of details for vehicular networking compared with SUMO and VISSIM [96].
research. STRAW street topology modeling uses real life
TIGER street maps collected by the US Census Bureau. It
VII. S MART V EHICLES AND TMS I NTERACTION
includes streets whose structure allows for identification of
segments, ramps, intersections, etc. STRAW models vehicular Vehicular communication can play an essential role in
node movement including acceleration, deceleration, etc. The improving the efficiency of both data collection and TMS
mobility model addresses aspects such as vehicular congestion reaction to some circumstances or emergency events. Smart
and traffic control by deploying specialised mechanisms to vehicles are usually equipped with on board sensors that are
impose infrastructure limitations on the traffic flow [94]. able to detect both in-vehicle events as well as the surrounding
4) VISSIM: VISSIM is a microscopic, behaviour-based dis- traffic conditions. These inner events such as sudden decel-
crete event traffic simulation system modeling motorway and eration and airbag tripping are immediately reported to the
urban road traffic. Based on complex mathematical models, neighbouring vehicles and the Road-Side Units (RSUs). On

Table III: A summary of the main features and limitations of road traffic microscopic simulation tools and applications

VANETMobisim STRAW SUMO PTV Vissim SIDRA TRIP

Transport Modes Truck & Cars Cars Multi-modal Multi-modal Cars

Accuracy Time step of 1ms Time step of 1s Time step of 0.1s Time step of 1s

Scalability Medium Medium High Very High Very low

Up to 100,000 vehicles No-built in limits Single car

GUI Limited Limited 2D view 2D&3D view 2D view

Realistic simulation of No No No Yes No

pedestrian and passengers behaviors

Parking management No No No Yes No

C2X support No No No Yes No

Accurate analysis of No No No No Yes

single cars trip

Popularity Medium Low Very high Medium Low

Licence needed No No No Yes Yes

the other hand, the received events from other vehicles or approaching vehicles, in order to reduce their waiting time
road sensors are processed and reported similarly to the inner when they reach the intersection. In this case, the vehicles
ones. The gathered traffic data from smart vehicles are then are informed about the optimal speed which allows them to
analyzed and combined with other data feeds in order to speed cross the intersection without stopping. To achieve this goal,
up traffic congestion detection and improve the congestion the vehicles need to coordinate between each other to adjust
levels evaluation accuracy. In this context, these data need their current speed according to the speed advised by the
to be quickly disseminated with high transmission reliability, infrastructure. The purpose of the coordination between the
especially if it reports safety critical events. Thus, appropriate vehicles is to avoid collision when they adapt their speed
dissemination protocols are required. In what follows, we according to the information received from the traffic light
discuss a set of scenarios in which the interaction between controller.
the TMS and smart vehicles will significantly help to reduce One of the most critical consequences of traffic congestion
traffic congestion and improve roads safety. is the delay of emergency services, such as police intervention,
In the first scenario, we propose to investigate the possibility fire and rescue operations as well as medical services. This
of affecting/changing the cars behavior (e.g. speed, optimal scenario aims to reduce the latency of emergency services
route etc) and the driving policies (e.g. maximum speed, delivery by dynamically adjusting traffic lights, changing
minimum speed, reserved lanes etc) rather than only closing related driving policies, recommending behaviour change to
some road segments as proposed in the legacy systems. In this drivers, and applying essential security controls [28]. This
case, the cars need fast and accurate coordination when they will create green route for these vehicles and significantly
change lanes to temporarily use a lane which was reserved reduce their response time, which may save human lives and
for buses or slow cars, in order to prevent crashes. To this reduce the induced damage/loss in case of fire or robbery.
end, a real-time dissemination of lane change notification is a The TMS should be also able to control the behaviour of non-
must since lane change in this context may lead to collision if emergency vehicles to ensure minimum number (ideally zero)
more than one car move to the same lane simultaneously and of crashes, minimum disruption to the regular traffic flow, and
without coordination. Moreover, the road-side infrastructure satisfaction of security requirements to prevent any misuse of
may also make use of the information exchanged between the the system. To make this scenario viable and valuable in real
vehicles through the transmission of beacon messages. It will road environment, some specific actions should be taken by
then combine the content of these beacons (i.e. vehicle speed, both TMS and smart vehicles in addition to some requirements
position, heading etc) with the reported data from the road which should be satisfied, such as:
monitoring equipment, as shown in Figure 1, to speed up the • The traffic light controller is made aware of the approach
congestion detection and improve its accuracy, and thus the of an emergency car through a special message sent by
TMSs can take early actions to control the traffic congestion. this car towards the infrastructure when it approaches the
In the second scenario, we propose that the road-side intersection. Alternatively, if an induction loop system is
infrastructure (e.g. traffic light controller at an intersection) in place we can imagine that those cars are equipped
communicates the remaining time for the current traffic light with a special tag (hardware) to distinguish them from
cycle (i.e. to switch from green to red and vice versa) to the the other cars. Hence, whenever an emergency car passes

through the induction loop system this latter automati- and management, leading to an increasing number of vulner-
cally generates a special message to request the traffic abilities at several levels. For example, the use of wireless
light controller to switch to green or to extend the green sensors for data sensing and gathering may lead to several
light cycle till the emergency car crosses the intersection. attacks inherited from WSNs technology and wireless multi-
• Fast and reliable V2V and V2I communication protocols hop communication paradigm, which affects both the integrity
are needed to enable real-time interactions between the and quality of the collected traffic data. Moreover, leveraging
TMS and the smart vehicles. smart vehicles for spreading warning notifications about on-
• Adequate security mechanisms should be added to this roads emergency events may lead to severe consequences that
system to prevent its misuse by malicious cars that may range from increasing traffic jams to economic damages and
spoof the identity of an emergency car for different human lives loss in case of robbery or terrorist attacks. For
purposes. example, a vehicle advertising an accident in a given road
• The TMS should apply advanced decision-making so- segment not covered by visual monitoring equipment may
lutions to find alternative routes to divert the normal succeed to divert the traffic from this particular area in order
vehicles from the dangerous area in order to protect the to undertake a criminal act or just create traffic jam in the
drivers lives and ease the access for emergency vehicles. surrounding. Indeed, road transportation networks are very
These solutions must consider the real-time contextual attractive targets for criminals aiming to inflict big loss to
factors as well as the security requirements. the city and road authority, serious panic among population
An example of an adaptive TMS in emergency scenario is and create spectacular media images, as those networks are
depicted in Figure 9, in which the Local Traffic Controller usually used by large numbers of cars (drivers) at predictable
(TLC) uses the gathered information about the traffic condi- times in predictable places, especially in big cities.
tions to clear the way for the ambulance. This can be achieved Furthermore, traffic-aware or context-aware content security
by changing the traffic light cycles, and defining new driving has recently attracted a lot of attention from the research
policies and announcing them to the cars through the set of community and several issues have been identified in this
RSUs deployed along the roadside. regard. In [142], the authors revealed that VANETs can be an
easy target of indirect attacks through exploiting the sensors
VIII. S ECURITY T HREATS AGAINST TMS deployed for traffic information collection and reporting. A
Secure and highly efficient TMSs, which are responsible malicious user can, in this case, remove/drop certain sensor
for critical operations such as transportation infrastructures readings indicating traffic congestion in a given area, or
supervision and road traffic control, are essential to strengthen spoof the identity of some road traffic monitoring sensors and
the national security of any country in the world and support insert fake values indicating a traffic jam in road segments
its economic expansion since both governmental and private with low traffic, which may mislead other vehicles as well
companies rely on these infrastructures to successfully ac- as traffic controllers and lead to devastating consequences.
complish their daily operations. However, both TMSs and Besides these security threats, Sybil attack is another type
road infrastructures are vulnerable to a bunch of threats that of attacks very hard to detect especially in such highly dy-
range from environmental and accidental events to malicious namic environment like VANETs. To cope with it, data-centric
attacks, and may lead to sustained outages and wide disruption. misbehaviour detection schemes have been applied. The two
Advanced TMSs and Traveler Information Systems (TISs) main mechanisms used are consistency check and plausibility
exploit the technologies used by transportation infrastructures check. The former mechanism checks the consistency of traffic
to enable real-time collection and dissemination of information information reported by vehicles in the same area and finds
about traffic flow conditions and transit schedules, in order out any deviating values reported by either malicious or faulty
to decrease the congestion level and traffic incidents. Addi- nodes. The latter mechanism usually has a model of the real
tionally, TMSs may provide other services for public transit world used to check whether the reported values comply with
systems, commercial vehicle systems as well as emergency this model and thus detect any unrealistic values advertised
management systems. Any disruption of these services can by misbehaving vehicles. In addition to the above schemes,
lead to destructive impact on public safety and/or national [143] has proposed novel scheme to deal with malicious nodes
economy depending on the target system. These disruptions attempting to spread forged messages in VANETs for both
can be caused by hackers, terrorists, foreign enemies, or safety and non-safety applications. To this end, the bilinear
unauthorized users, and can be a consequence of power failure, pairing has been used to ensure fast and accurate verification of
natural disaster like a storm or tornado, or a telecommunication the authenticity of the messages content. This scheme assumes
outage. that each vehicle is equipped with a tamper-proof device. The
Despite the efforts of road and public authorities to rein- obtained results have proven its effectiveness and supremacy
force transportation systems security, they are still prone to over the existing solutions in the literature.
numerous threats that may target the critical road infrastructure As discussed above, TMS is vulnerable to numerous secu-
including the monitoring equipment, the connected smart rity attacks that exploit the vulnerabilities of the equipment and
vehicles system, the smartphones based ITS applications, or technologies involved in its operations. These attacks can be
the core of the TMS in order to bring it to halt. These categorized into three main categories according to the target
threats become more serious with the recent trends on in- entity. The first category concerns the attacks targeting the crit-
tegrating advanced technologies for road traffic surveillance ical road infrastructure through an unauthorized access to or

Figure 9: Example of Adaptive TMS

malicious misuse of the monitoring equipment such as wireless using smart phones as traffic probes for more accurate traffic
sensors, M2M devices and surveillance cameras. The second congestion estimation have raised a particular concern about
category consists of the attacks launched against the smart the privacy of the users. In the context of a TMS, the use
vehicles being used as source/destination of traffic information of smartphones entails also the risk that anyone can join the
by spreading forged information about traffic congestion level, system and start sending its location samples. This means
incidents etc. Finally, the last category includes the attacks that the system is exposed to potential reporting of forged
aiming at breaking down the key components needed for location data by misbehaving users, which may lead to inac-
TMS operations (i.e. the core system which manages both curate assessment of the real traffic conditions. Consequently,
the road infrastructure and the monitoring equipment). Cyber erroneous traffic information will be spread by the TMS
attacks are the most severe threats for the core system of towards the drivers resulting in traffic conditions deterioration
TMS since a successful infiltration, through any cyber defence as well as traffic incidents in some extreme cases. Mobile
breaches, will give criminals full control of the transportation users location privacy and the threats against it have recently
infrastructure, which would cause massive loss of data and raised an increasing attention due to the numerous location-
serious damage to physical assets in addition to potential aware applications that have been designed for smartphones.
human lives loss. Several worms have been developed to To protect their privacy, the drivers tend usually to have their
launch cyber attacks against critical systems such as the exact position obfuscated to prevent being tracked by a third
”Stuxnet” worm, ”Duqu”, ”Flame” and ”Gauss” viruses. To party. This obfuscation will significantly reduce the accuracy
cope with the increasing threat of these sophisticated worms, of real-time traffic conditions estimation. Therefore, robust
conventional security solutions such as anti-virus softwares privacy preservation techniques are required to reassure the
and firewalls are, unfortunately, not sufficient. Therefore, more users and incite them to disclose their exact position.
robust countermeasures are needed to defeat these cyber
attacks. Trust management of mobile users is another issue for
TMS in order to deliver reliable decisions and ensure better
Besides the above security threats, the recent trends of control of the traffic flow. The trust management task will

enable the TMS to establish a list of reliable mobile data design, implementation and deployment aspects. The very
sources, according to periodic evaluation of their trust level, recent project Accelerate Cooperative Mobility (DRIVE C2X)
in addition to roads monitoring equipment such as, sensors, [64] goes beyond the previous projects which have proven the
CCTV cameras, induction loops etc. A misbehaving driver feasibility of safety and traffic efficiency applications based
(e.g. terrorist, robber) may use his smartphone to broadcast on vehicular communications and addresses large-scale field
fake information in order to re-route the other cars to clear trials under real-world conditions at multiple national test sites
the way for the terrorists’/robbers’ vehicle, or just divert the across Europe.
traffic towards a specific road segment to create a bottleneck.
As the TMS will not react to information sent by a non-trusted B. Safety
data source, the misbehaving driver may spoof the identity
The COOPerative SystEMS for Intelligent Road Safety
of another reliable data source to ensure that his goal will
(COOPERS) [113] is a research and development project in
be achieved. In this case, the lack of adequate authentication
the area of cooperative and in-vehicle integrated safety systems
mechanisms will be of detrimental impact.
funded by the European Commission FP6 programme in 2006.
COOPERS focuses on the development of innovative telem-
IX. R ELATED E UROPEAN /I NTERNATIONAL INITIATIVES atics applications based on communication between vehicles
( PROJECTS ) and infrastructure, which will bring together experts from both
In this section, we present a snapshot of recent projects that car industry and infrastructure operators. Ultimately the goal
aim to improve the different aspects of a traffic management of COOPERS is the enhancement of road safety by direct
system. The projects have been organised based on their and up to date traffic information communication between
major concern in terms of architecture, safety, efficiency, infrastructure and motorised vehicles.
sustainability and energy-awareness, reliability and security The Cooperative Vehicles and Road Infrastructure for Road
and innovative services. Table IV summarises these projects. Safety (SAFESPOT) [114] is an FP6 integrated research project
co-funded by the European Commission Information Soci-
A. Architecture ety Technologies programme. SAFESPOT focuses on road
accidents prevention via an online assistant which extends
The Keystone Architecture Required for European Networks
the drivers’ awareness of the surroundings in both space and
(KAREN) project [110] made the first steps towards an inte-
time and detects potentially dangerous situations in advance.
grated ITS architecture between 1998 and 2000. KAREN has
SAFESPOT makes use of vehicle to vehicle and vehicle to
addressed the need for a single reference platform in Europe,
infrastructure communications. The European Commission-
which would provide a basis for the development of ITS
funded Network of Excellence on Advanced Passive Safety
products and services. The Framework Architecture Made for
(APSN) [128] has established an integrated European Vir-
Europe (FRAME-NET) project [111] has gathered a thematic
tual Centre of Excellence on vehicle passive safety research
network of interested parties funded by the European Union
and development in 2006. APSN goal was to accelerate the
Fifth Framework programme (FP5), which have coordinated
improvements in road safety in order to reduce the annual
and promoted wide scale implementation of ITS architecture-
road victims in the European Union. Advanced Protection
related activities in Europe starting from July 2001. The
SYStems (APROSYS) [129] is a FP6 integrated project that
Framework Architecture Made for Europe - Support (FRAME-
has developed and introduced critical scientific and technology
S) [111] has extended the original ITS architecture and updated
developments that improve passive safety for road users in
it to include the latest requirements from functional, physical
all-relevant accident types in Europe. The Save Our Lives - A
and communications points of view.
Comprehensive Road Safety Strategy for Central Europe (SOL)
Extending the FRAME architecture (E-FRAME) [111] is
[112] is an on-going Central European project whose goal is
another three year European-funded project which has further
to promote sustainable mobility, increase awareness for safety
extended the FRAME ITS architecture to support the creation
issues, and contribute to the achievement of higher quality of
of inter-operable and scalable cooperative systems throughout
living conditions for road users.
the European Union. The project which started in 2008 has
focused on acquiring, exchanging, and processing data from
vehicles (e.g. road conditions) for the benefit of the driver (e.g. C. Sustainability and Energy-awareness
better driver information and trip planning) and third parties The ”Partners for Advanced Transportation TecHnology
(e.g. knowledge of road network state). Very important is the (PATH)” [68] multi-disciplinary large scale research and de-
integration role of this project as the updated ITS architecture velopment program, which involves collaboration between
includes cooperative systems services and applications devel- universities, private industry, state and local agencies, and
oped by other European projects such as COOPERS [113], non-profit institutions from California, USA. PATH proposes
CVIS [121] and SAFESPOT [114]. Also the Preparation for state of the art research solutions to the surface transporta-
Driving Implementation and Evaluation of C2X Communi- tion systems problems. PATH focuses on the relatively long-
cation Technology (PREDRIVE C2X) FP7 integrated project term, high-impact solutions, and on the evolutionary steps
[64] has established a pan-European architecture framework that are required to have the long-term solutions deployed.
for cooperative systems, setting the road for field operational Some of PATH research focuses on fuel saving and transport-
tests on cooperative systems by focusing on architectural related gas emissions reduction. The Connect and Drive

[115] is a collaborative project between Dutch companies vehicles and between vehicles and roadside infrastructure. This
and universities, sponsored by the Dutch Government, which enables transparent IP connectivity between a vehicle and the
has developed technologies for Cooperative Advanced Cruise infrastructure, even using multi-hop or cache-based solutions.
Control (CACC). CACC has extended the functionality of
the Adaptive Cruise Control (ACC) based on communication E. Innovative Services
between vehicles in addition to sensor capabilities of each
vehicle to adapt the speed to other vehicles. The goal of The Adaptive Integrated Driver vEhicle interface (AIDE)
the project was to optimize traffic throughput, improve traffic [125] is an European project which has designed, developed
safety and reduce emission of vehicles. and validated an adaptive driver-vehicle interface system that
brings in the potential benefits of many new in-vehicle tech-
The Cooperative Mobility Systems and Services for Energy
nologies and nomad devices in terms of mobility and comfort
Efficiency (eCoMove) [119] is an FP7 European Commission-
in an efficient and integrated manner, without compromising
funded project which makes use of the latest vehicle-to-
safety. The Integrated Wireless and Traffic Simulation Platform
infrastructure and vehicle-to-vehicle communication technolo-
for Real-Time Road Traffic Management Solutions (iTETRIS)
gies in order to create an integrated energy-saving road traffic
[65] is an European FP7-funded project which has developed
solution. eCoMove includes eco-driving support and eco-
an open, ETSI standard compliant, and flexible simulation
traffic management in its endeavour to reduce energy waste
platform that integrates wireless communications and road
by passenger and goods vehicles. Lately, there is a significant
traffic simulation technologies and solutions in a common en-
push towards Full Electric Vehicles (FEV) and many FEV-
vironment that is easily tailored to specific situations allowing
related research projects are on-going. Among these works the
performance analysis of cooperative ITS at the level of a city.
Combining Infrastructure for Efficient Electric Mobility (eCo-
The Developing Next Generation Intelligent Vehicular Net-
FEV) [120] is a FP7 European Commission funded project
works and Applications (DIVA) is an on-going Canadian
which aims at achieving a breakthrough in the FEV space by
NSERC-funded research network which targets the develop-
proposing a general architecture for integration of FEV into the
ment and integration of communication systems, vehicular
different cooperating infrastructure systems. This architecture
technologies, and applications for enabling nationwide de-
makes use of state of the art communications technologies
ployment of vehicular ad-hoc networks and intelligent trans-
in order to support precise FEV telematics and charging
portation systems. Its focus ranges from developing innovative
management services based on the real-time information. The
large-scale communication architectures and wireless network
project will complete in 2015.
technologies to proposing solutions increasing the efficiency
and safety of Canada’s transportation systems.
D. Efficiency, Reliability and Security The Road Safety Attributes Exchange Infrastructure in
The Cooperative Vehicle Infrastructure Systems (CVIS) Europe (ROSATTE) is a FP7 European Commission-funded
[121] is a large European Commission-funded FP6 integrated project which has defined and implemented infrastructure and
project that has designed, developed and tested technologies supporting tools to ensure the efficiency and quality assurance
which support vehicles to communicate with each other and in the data supply chain from public authorities to commercial
with the nearby road infrastructure efficiently. By using CVIS map providers with regards to safety related road content.
technology the vehicles can communicate the latest traffic The on-going IBM Smarter City [69] project includes a large
information and safety warnings to road operators and other variety of initiatives, including traffic management, on a global
nearby vehicles, connecting through a multi-channel terminal scale. In particular traffic-related research and development
with a wide range of potential carriers, including cellular net- focuses on traffic modelling and simulation, smarter parking,
works (GPRS, UMTS), wireless local area networks (WiMax, maximizing revenue and minimizing environmental impact,
Wi-Fi), short-range microwave beacons (DSRC) or infrared integrated fare management, real-time traffic updates, reducing
(IR) based on the international ISO CALM standards. the commute time, improving mobility within a city, etc.
The Highly Dependable IP-based Networks and Services The overall aim is to contribute towards realising a smarter
(HIDENETS) [123] is a FP6 European Commission-funded transportation system for the 21st century.
project which has developed and analyzed end-to-end re-
silience solutions for distributed applications and mobility- X. O PEN C HALLENGES
based services in vehicular environments. The Secure Vehicu- This section identifies some open challenges existing re-
lar Communications (SeVeCom) [124] is an EU-funded project search in the area of TMS for smart cities faces and discusses
that has focused on providing full definition and implemen- potential avenues to putting additional effort toward finding
tation of security requirements for vehicular communications. highly sought after solutions. These challenges are presented
The project goal was to develop technologies to improve road in terms of the following major stages related to traffic
safety and optimise road traffic by making use of vehicular to data: gathering, storage, aggregation, exchange, processing and
infrastructure and inter-vehicular communications. The Geo- application-layer support.
addressing and geo-routing for vehicular communications A significant challenge in terms of information gathering
(GeoNET) [122] is a recent European FP7 project which has is related to the number of entities which collect traffic-
focused on a geographic addressing and routing protocol with based data, from road traffic operators such as public transport
support for IPv6 to be used to deliver safety messages between companies, private taxi companies, etc., and public traffic

Table IV: Major ITS-related Projects

Project Time period Area of concern Reference

KAREN (Keystone Architecture Required for European Networks) 1998-2000 Architecture [110]

FRAME-NET (Framework Architecture made for Europe) 2008-2011 [111]

CarCoDe (Platform for Smart Car to Car Content Delivery) 2012-2015 [66]

COOPERS (Cooperative systems for Intelligent Road Safety) 2006-2010 [113]

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management authorities such as local councils, planning insti- by proposing standards for representation and storage, but their
tutions, etc., to health and environment monitoring institutions, adoption is very limited to date.
such as health boards, environmental protection agencies, etc. Data storage suffers from the same problems and has the
and private companies and individuals. All these data gathering same open challenges with information gathering, as these
entities use independent measuring methods which acquire two stages are highly inter-connected. The only issue which
various data with different characteristics and using diverse is strictly related to storage is the database support. In this
methodologies and save it in their own databases. Relative regard there are several widely researched solutions, of which
simple issues such as data formatting in the absence of a the XML-based ones are the most popular. Open challenges
general accepted standard for the representation of traffic- are mostly performance-related, especially in a distributed
related data results in significant problems for its potential environment.
utilization by third parties. The most important consequence Data aggregation poses additional challenges to those re-
of this lack of a common format is the difficult synchronization lated to data gathering and storage. As the data originates from
of the information gathered by various sources, which makes different sources, their conversion is the most important next
almost impossible coherent usage of information and cross- step. In this process, the first obstacle is the amount of data
correlation of events. There are steps forward in this direction collected which is increasing exponentially and the second its

complexity. This makes data conversion increasingly difficult the open challenges need to be addressed as well as the
and highly time and resource consuming. In this stage relevant major international research projects dealing with TMS related
data extraction and cleaning, as well as data reduction might be challenges are presented.
required. Each of these tasks has its own challenges including
defining what is relevant and what is noise, identifying one or XII. ACKNOWLEDGEMENT
the other and extracting the useful data, given certain accuracy
This work was supported, in part, by Science Foundation
expectations. The latest interest surge in big data research
Ireland grant 10/CE/I1855 to Lero - the Irish Software Engi-
provides solutions to be also used in TMS for smart cities.
neering Research Centre (www.lero.ie).
Data exchange has attracted significant attention from many
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2009.

Dr. Soufiene Djahel is an engineering research John Murphy is an Associate Professor in Com-
manager at University College Dublin and member puter Science and Informatics at University College
of wireless networking group of PEL since February Dublin. He received a first class honours degree
2012. Before joining in PEL, he was a postdoc in electronic engineering (B.E.) in 1988 from the
fellow at ENSIIE where he was involved in a re- National University of Ireland (UCD), an M.Sc. in
search project aiming at designing communication electrical engineering from the California Institute
protocols for Hybrid Sensor and Vehicular Networks of Technology in 1990 and a Ph.D. in electronic
(HSVNs). He got his Ph.D degree in computer engineering from Dublin City University in March
science in December 2010 from LILLE 1 University- 1996. He is an IBM Faculty Fellow, a Fellow of
Science and Technology of France. During his Ph.D, the Institution of Engineering and Technology, a
he was working on security issues at MAC and Senior Member of the IEEE, a Fellow and Chartered
Routing layers in wireless multi-hop networks. Prior to that, he spent 6 Engineer with Engineers Ireland, and a Fellow of the Irish Computer Society.
months at INRIA NORD Europe research center as an engineer researcher. He For many years he held an academic part-time position at the Jet Propulsion
received a Magister degree with majors in networking and distributed systems Laboratory in Pasadena, and acted as a consultant to the US Department of
and a state engineering degree in computer science from Abderrahmane- Justice.
mira University (Bejaia, Algeria) and Badji-Mokhtar University (Annaba, Prof. Murphy is an editor for both IEEE Communications Surveys and
Algeria) in February 2007 and June 2004, respectively. The research interests Tutorials (since 2012) and Telecommunications Systems Journal (since 2008),
of Soufiene Djahel include Intelligent Transportation Systems, Security and and a guest editor for an upcoming issue on ’Automation in Software
QoS issues in Wireless Networks (VANETs, MANETs, WSNs and WBANs) Performance Engineering’ in the Automated Software Engineering Journal
and Internet of Things. He is member of IEEE and reviewer of its major (2014). He has served on the Editorial Board of IEEE Communications Letters
conferences and journals in wireless networks and security. He was the general (2008-2012) and IET Communications (2006-2010), where he was a guest
co-chair of VTM 2014 and the TPC co-chair of VTM 2012, and has served editor (with Prof. Perros) for ’Optical Burst and Packet Switching’ in 2009.
on the TPC of several conferences including IEEE ICC, IEEE WCNC, IEEE He has published over 100 peer-reviewed journal articles or international
Globecom and IEEE IWCMC. conference full papers in performance engineering of networks and distributed
systems. He has supervised 17 Ph.D. students to completion and been awarded
over 20 competitive research grants (in excess of 7.5 million euro)

Ronan Doolan received his bachelors degree in

Mechatronic Engineering from Dublin City Univer-
sity in Ireland in 2011. He is currently a PhD.
Candidate in the performance engineering laboratory
in DCU. He is funded by LERO the Irish software
foundation (Grant no. 10/CE/11855). His research
interests include Vehicular Ad Hoc Networks, Elec-
tric Vehicles, Vehicular Routing, Smart Cities.

Dr. Gabriel-Miro Muntean is a Senior Lecturer

with the School of Electronic Engineering, Dublin
City University (DCU), Ireland, where he obtained
his Ph.D. degree in 2003 for research on quality
oriented adaptive multimedia streaming over wired
networks. He was awarded the B.Eng. and M.Sc.
degrees in Software Engineering from the Computer
Science Department, ”Politehnica” University of
Timisoara, Romania in 1996 and 1997 respectively.
Dr. Muntean is co-Director of the DCU Performance
Engineering Laboratory and Consultant Professor
with the Beijing University of Posts and Telecommunications, China. His
research interests include quality-oriented and performance-related issues of
adaptive multimedia delivery, performance of wired and wireless communi-
cations and energy-aware networking. Dr. Muntean has published over 180
papers in prestigious international journals and conferences, has authored three
books and sixteen book chapters and has edited six other books and conference
proceedings. Dr. Muntean is Associate Editor of the IEEE Transactions on
Broadcasting, Associate Editor for the IEEE Communication Surveys and
Tutorials and reviewer for other important international journals, conferences
and funding agencies. He is a member of IEEE, ACM and IEEE Broadcast
Technology Society.

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ITM Challenges and Approaches

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ITM Challenges and Approaches

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This article has been accepted for publication in a future issue of this journal, but has not been

A Communications-oriented Perspective on Traffic

Figure 1: Data life cycle in smart transportation

especially in developing countries. Moreover, the gathered incidents on the roads.

Figure 2: A simple architecture of a modern TMS

to whether the forecasting model will be implemented as part

Table I: Comparison of existing traffic prediction approaches

level environment constraints data source issues

ARIMA Low Medium Yes Highway High Yes Low

Neural Networks Low Medium No Highway High Yes Low

Statistical models Medium High Yes Urban area Medium No Medium

GPS data High High No Urban area High No High

Figure 6: Route planning algorithms: main inputs and functioning

Figure 7: Illustration of Prediction, Routing and Parking services cooperation

Communication Computation Scalability Latency Delivery Network partitioning Target Infrastructure

overhead overhead level ratio resiliency scenario dependant

GPCR [3] Low Low Medium High Low No Urban No

3rule [86] Low Low Unknown Unknown High No All Yes

LORA-CBF [4] Medium Low High Low High Medium Urban No

VADD [7] Low Medium Medium Medium Low High Rural No

V-TRADE [16] Medium Low Medium Medium Low No Highway No

PDGR [14] Medium Medium Medium Medium Medium No Urban No

MURU [18] Low Medium Medium Low Medium High Urban No

A-star [19] Medium Low Medium Medium Low Medium Urban No

IVG [21] Low Low High Low Medium High Highway No

GyTAR [22] Low Low Medium Low Medium High Urban No

VANETMobisim STRAW SUMO PTV Vissim SIDRA TRIP

Transport Modes Truck & Cars Cars Multi-modal Multi-modal Cars

Scalability Medium Medium High Very High Very low

Up to 100,000 vehicles No-built in limits Single car

GUI Limited Limited 2D view 2D&3D view 2D view

Realistic simulation of No No No Yes No

pedestrian and passengers behaviors

Parking management No No No Yes No

C2X support No No No Yes No

Accurate analysis of No No No No Yes

single cars trip

Popularity Medium Low Very high Medium Low

Licence needed No No No Yes Yes

Figure 9: Example of Adaptive TMS

Table IV: Major ITS-related Projects

Project Time period Area of concern Reference

FRAME-NET (Framework Architecture made for Europe) 2008-2011 [111]

COOPERS (Cooperative systems for Intelligent Road Safety) 2006-2010 [113]

CVIS (Cooperative Vehicle Infrastructure Systems) 2006-2010 [121]

SAFESPOT (Cooperative Vehicles and Road Infrastructure) 2006-2010 [114]

DRIVE C2X 2010-2013 [64]

APSN (Network of excellence on advanced passive safety) 2004-2008 Safety [128]

APROSYS (Advanced protection systems) 2004-2009 [129]

CACC (Cooperative Advanced Cruise Control) 2009-ongoing Energy-awareness [115]

eCo-FEV (Combining infrastructure for efficient electric mobility) 2012-2015 [120]

SeVeCom (Secure Vehicular Communications) 2006-2009 and Security [124]

traffic management solutions)

ROSATTE (Road Safety Attributes Exchange Infrastructure in Europe) 2008-2010 [126]

IBM Smarter Cities [69]

Ronan Doolan received his bachelors degree in

Dr. Gabriel-Miro Muntean is a Senior Lecturer

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