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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
mobility have determined a rapid increase in the number of
vehicles on the roads, which has resulted in many challenges
for road traffic management authorities in relation to traffic
congestion, accidents and air pollution. Over the recent years,
researchers from both industry and academia were focusing their
efforts on exploiting the advances in sensing, communication
and dynamic adaptive technologies to make the existing road
Traffic Management Systems (TMS) more efficient to cope with
the above issues in future smart cities. However, these efforts
are still insufficient to build a reliable and secure TMS that
can handle the foreseeable rise of population and vehicles in
smart cities. In this survey, we present an up to date review of
the different technologies used in the different phases involved
in a TMS, and discuss the potential use of smart cars and
social media to enable fast and more accurate traffic congestion
detection and mitigation. We also provide a thorough study
of the security threats that may jeopardize the efficiency of
the TMS and endanger drivers’ lives. Furthermore, the most
significant and recent European and worldwide projects dealing
with traffic congestion issues are briefly discussed to highlight
their contribution to the advancement of smart transportation.
Finally, we discuss some open challenges and present our own
vision to develop robust TMSs for future smart cities.
Index Terms—Traffic Management System (TMS), Smart
Cities, Smart Transportation, Data Sensing and Gathering,
VANETs, Route Planning, Traffic prediction.
I. INTRODUCTION
S
MART cities is a label that is associated with a significant
paradigm shift of interest towards proposing and using
various innovative technologies to make cities ”smarter” in
order to improve the people’s quality of life. As a very impor-
tant and highly visible initiative, the European Commission
has launched the European Initiative on Smart Cities in 2010
[1] that addresses four dimensions of the city: buildings,
heating and cooling systems, electricity and transport. Strictly
related to transportation, the goal is to identify and support
sustainable forms of transportation, to build intelligent public
transportation systems based on real-time information, Traffic
Management Systems (TMS) for congestion avoidance, safety
and green applications (e.g. to reduce fuel consumption, gas
emissions or energy consumption).
In this context, it is worth noting that the number of
cars using the limited road network infrastructure has seen
Soufiene Djahel and John Murphy are with Performance Engineering
Laboratory, University College Dublin, Ireland.
Ronan Doolan and Gabriel-Miro Muntean are with Performance Engi-
neering Laboratory, Dublin City University, Ireland.
Manuscript received November 2013.
a tremendous growth. One major consequence of this in-
crease is related to management problems that range from
traffic congestion control to driving safety and environmental
impact. Over recent years, researchers from both industry
and academia were focusing their efforts on leveraging the
advances in wireless sensing equipment and communication
technologies, along with simulation and modeling tools to
make the existing road TMS more efficient, enabling them to
cope with the above issues in future smart cities. One of the
most critical consequence of traffic congestion is the delay of
emergency services, such as police, fire and rescue operations,
medical services, etc. Indeed, very often individual human
lives, general population safety and institutional economic or
financial situation in case of incidents, robberies or criminal
attacks highly depend on the efficiency and timely response
of emergency vehicle services. Additionally, recent road traffic
statistics reveal another extremely serious concern which is the
increasing number of vehicle crashes. These crashes usually
happen in the areas around congested roads as the drivers tend
to drive faster, before or after encountering congestions, in
order to compensate for the experienced delay. The negative
consequences of these accidents are many, at personal, group
and societal levels, and could be exacerbated if emergency
vehicles are involved in a crash.
However, most large cities in the world are still suffering
from traffic congestion, despite employing different solutions
to reduce it, including using TMSs deploying advanced con-
gestion control mechanisms. In order to best contribute to
the ongoing efforts to solve the traffic congestion problem
or at least reduce its impact, there is a need to understand
the different types of congestion and their impact. Two major
types of congestion can be distinguished: recurrent and non-
recurrent. Recurrent congestion usually occurs when a large
number of vehicles use the limited space of the road network
simultaneously (e.g. weekday morning and afternoon peak
hours). Non-recurrent congestion mainly results from random
events such as traffic incidents (e.g. car crash or a stalled
vehicle), work zones, bad weather conditions and some special
events like sport events, Christmas, etc. According to recent
statistics (http://www.transport2012.org), road traffic conges-
tion costs billions to the world economy. For instance losses
have reached:
• 200 e billions in Europe (2% of GDP)
• $101 billion in USA
Aggregate delays of 4.8 billion hours were experienced
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and 1.9 billion gallons of fuel were wasted worldwide. These
statistics give a clear indication of the devastating impact that
congestion has on individuals, companies (e.g. freight and
transport companies, etc.) and society.
Unfortunately, to date the existing TMSs do not provide
sufficient and accurate road traffic information to enable
granular and timely monitoring and management of the road
traffic network. Some of the reasons include: lack of granular
data collection, inability to meaningfully aggregate much of
the data collected, and a lack of complex management systems
capable of providing accurate views of the road transport
network. This inability to effectively monitor and manage
the traffic maintains traffic congestion high, which in turn
affects road safety (i.e. increases the number of death on
the roads), augments fuel consumption and causes large gas
emissions. The main solutions used by the existing TMSs
to manage the traffic after an incident or during peak hours
is changing/adapting traffic lights cycles, closing road lanes
and intersections, etc. These solutions have limited efficiency
when the increasing number of cars are using the limited
road infrastructure and constantly new solutions to be used
by TMSs are being proposed by the research community.
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,
from information gathering to service delivery. In particular
the paper discusses the Data Sensing and Gathering (DSG)
phase in which heterogeneous road monitoring equipment
measure traffic parameters (such as traffic volume, speed and
road segments occupancy, etc.), and periodically report these
readings to a management entity. These monitoring tools can
detect random incidents and immediately report them through
broadband wireless networks, cellular networks or mobile
sensing applications. As these data feeds are fused and aggre-
gated during the Data Fusion, Processing and Aggregation
(DFPA) phase to extract useful traffic information, the paper
analyses this phase in detail. The Data Exploitation (DE)
phase uses the acquired knowledge from the data processing
phase to compute optimal routes for the vehicles, short-term
traffic forecasts, and various other road traffic statistics. Finally
in the Service Delivery (SD) phase, the TMS delivers this
knowledge to the end users (such as drivers, authorities,
private companies, etc.) using a variety of devices such as
smart phones, vehicle on-board units, etc. Moreover, the paper
investigates the advantages of using alternative approaches,
such as mobile sensing and social media, to improve TMS’s
efficiency and accuracy. This survey also discusses the security
attacks that may threaten the integrity of traffic data, leading
to non-optimal and incorrect decisions taken by the TMS in
relation to the detected/reported incidents. Furthermore, the
most significant and recent projects trying to address traffic
congestion are briefly discussed, highlighting their contribu-
tion to the advancement of TMS. Finally, open challenges are
noted and the authors’ vision on robust TMS development for
future smart cities is presented.
The remainder of this paper is organized as follows. In the
next section, we give an overview of future TMSs, highlighting
their important conceptual phases and design stages. Then, we
address the Data Sensing and Gathering phase with a brief
description of the different technologies used for road traffic
and events monitoring, and discuss alternative technologies
that may improve the quality and accuracy of the collected
data. Afterwards, we discuss Data Fusion, Processing and Ag-
gregation techniques, followed by a description of the services
that a TMS may provide based on the collected and fused
data, including short term traffic prediction information, route
planning and parking management information, in sections
IV and V, respectively. In section VI, we investigate the
different routing approaches used in VANETs to exchange
the collected road traffic information among the vehicles, the
beacon congestion problem in IEEE 802.11p as well as the
simulation tools used for traffic and VANET-based application
simulation. Subsequently, we show how smart vehicles may
significantly improve the efficiency of current TMSs, in section
VII. In section VIII, we discuss the different threats that may
jeopardize the security and privacy of TMSs. In section IX, we
present the major international projects aiming at improving
the different aspects of future TMSs. In the final sections, our
vision on open challenges is discussed and this survey paper
is concluded.
II. OVERVIEW OF FUTURE TRAFFIC MANAGEMENT
SYSTEMS
A Traffic Management System (TMS) offers capabilities
that can potentially be used to reduce road traffic congestion,
improve response time to incidents, and ensure a better travel
experience for commuters. A typical TMS consists of a set
of complementary phases, as shown in Figure 1, each of
which plays a specific role in ensuring efficient monitoring
and control of the traffic flow in the city. The cornerstone
phase of a TMS is Data Sensing and Gathering (DSG)
in which heterogeneous road monitoring equipment measure
traffic parameters (such as traffic volumes, speed and road
segments occupancy, etc.), and periodically report these read-
ings to a central entity. For example, these monitoring tools
can detect random incidents and immediately report them
through wireless networks, cellular networks or mobile sensing
applications. Subsequently, these data feeds are fused and ag-
gregated during the Data Fusion, Processing and Aggregation
(DFPA) phase to extract useful traffic information. The next
phase, Data Exploitation (DE), uses this acquired knowledge
from the processed data to compute: optimal routes for the
vehicles, short term traffic forecasts, and various other road
traffic statistics. Finally in the Service Delivery (SD) phase,
the TMS delivers this knowledge to the end users (such as
drivers, authorities, private companies, etc.) using a variety of
devices such as smart phones, vehicles’ on-board units, etc.
The capabilities offered by a TMS are not confined to serve
drivers and road authorities only, but can also contribute signif-
icantly to the economic growth of a country, to the preservation
of citizens’ safety and to the support of national security. The
currently deployed technologies for road traffic surveillance
still suffer from a lack of traffic parameters measurement
accuracy and real-time report of events that occur on the roads,
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Figure 1: Data life cycle in smart transportation
especially in developing countries. Moreover, the gathered
traffic data usually needs to undergo a filtering process to
improve its quality and eliminate the noise. Deploying highly
sophisticated equipment to ensure accurate estimation of traffic
flows and timely detection of emergency events may not be
the ideal solution, due to the limitation in financial resources
to support dense deployment and the maintenance of such
equipment, in addition to their lack of flexibility. Therefore,
alternative cost-effective and flexible solutions are needed to
guarantee better management of road traffic in both developed
and developing countries.
A modern TMS aims to overcome some of the above
limitations by designing innovative approaches able to exploit
advanced technologies to efficiently monitor the evolving crit-
ical road infrastructure. These approaches should be scalable
enough in order to enable better control of the traffic flow
and enhanced management of large cities’ road networks. This
will certainly improve the accuracy of the acquired real-time
traffic information and the short-term traffic prediction. This
will enable making and using short-term predictions based on
current traffic volumes to identify bottlenecks and make more
informed decisions about how to best reroute traffic, change
lane priorities, modify traffic light sequences, etc. A modern
TMS should also provide a visual tool that can display in
real-time traffic information related to location of bottlenecks,
incidents, and congestion level in each road segment, as well
as estimated travel time from one location to another in
the road network. In this way, the transport authorities will
have an overall view of the road network in real-time, and
will enable the best support for improvements in the traffic
flow management and more efficient reactions to emergency
incidents on the roads.
An adequate TMS for future smart cities should fulfill the
following requirements:
• Ensure higher accuracy in estimating traffic conditions
and better efficiency in dealing with emergency situations
on the roads, compared to the existing TMSs.
• Be able to efficiently manage the traffic in road networks
of varying size and characteristics.
• Provide real-time road traffic simulation and visualisation
to help authorities more efficiently manage the road
infrastructure and improve route planning for commuters.
• Ensure simplified and smooth integration of existing
systems and new technologies, and manage the evolution
of these systems.
A high level architectural overview of a modern TMS is
depicted in Figure 2. This figure shows the main components
of the TMS needed to deliver the collected road traffic infor-
mation to the intended end consumers (e.g. road authorities,
Police, drivers etc). As we can see from this figure, the core
system of the TMS collects road traffic information from
heterogeneous data sources according to the consumer needs
and specific requests. These data feeds are then aggregated
and stored in an unified format in one or multiple databases.
Later, upon reception of a consumer request, the core system
processes the request and extracts the pertinent data from
the appropriate database. Then the requested information is
sent back to the intended consumer, tailored for their specific
purposes: e.g. analysis and statistics, decision-making, etc.
III. TRAFFIC DATA SENSING AND GATHERING
Data sensing and gathering phase focuses on the scalable
collection of traffic flow information from a large number of
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Figure 2: A simple architecture of a modern TMS
heterogeneous sources. Many of the current deployed systems
used by traffic management agencies collect data in a variety
of formats, time scales, and granularity. This is due to the
fact that those systems have been deployed at different time
periods with little or no integration between them. This creates
a management problem for operators whom must manage,
analyse and interpret all of this dissimilar data. A modern
TMS will analyse a number of the existing traffic informa-
tion collection mechanisms employed by city authorities and
identify where new technologies and systems can be used to
improve the accuracy, timeliness and cost efficiency of data
collection. In addition, these new data collection technologies
must provide a more informed explanation of the root causes
behind the increasing congestion levels on the roads. More
specifically, the current trends in TMS development consist in
leveraging advanced communication and sensing technologies
like Wireless Sensor Networks (WSNs), cellular networks,
mobile sensing and social media feeds as potential solutions
to circumvent the limitation of the existing systems.
The main wireless technology used for events sensing and
gathering on the roads is the tiny sensor devices. These sensors
could be mounted on vehicles, at the roadside or under the
road pavement to sense and report different events. In the
former case, the in-vehicles embedded sensors monitor and
measure several parameters related to the vehicle operations
and communicate them to the nearby vehicles or roadside
units. In the latter cases, the sensors are mainly used to
measure the passing vehicles’ speed, the traffic volumes as
well other environmental parameters. WSNs can be used to
interconnect these sensors and greatly reduce the cost of
monitoring systems deployment. In an urban scenario, we can
imagine a plethora of sensors being deployed to collect data
about traffic conditions, air pollution, environmental noise and
many other applications. Information can also be obtained
from vehicles that have proper sensors and communication
antennas on board; these would primarily be public transporta-
tion vehicles, taxis, police cars, and freight vehicles. A modern
TMS will, therefore, focus on designing innovative solutions
able to collect data from a specific region of interest under
specific time constraints, while minimising cost and spectrum
usage and maximising system utilisation.
A. Wireless Sensor Networks (WSNs)
Due to their high efficiency and accuracy in sensing the
different events, wireless sensors have been widely deployed
in various environments for data collection and monitoring
purposes [76], [77]. Indeed, it is foreseen that WSNs can
enable several applications that may significantly improve the
control of road traffic flow and ease its management, examples
of these applications are the real-time control of traffic lights
[73] and their adaptation according to the congestion level
[74], as well as parking spaces management [72]. However,
the deployment of wireless sensors in the road environment to
realize these applications face several challenges, in addition
to the well-known issues in WSNs [75], that require careful
consideration and design of appropriate protocols. Among
these challenges, we highlight the need of a fast and reliable
MAC access protocol [31] and data forwarding mechanisms
to guarantee timely transmission of critical messages carrying
information about the occurred emergency events on the road.
An example of WSNs deployment for road traffic monitoring
is shown in Figure 3.
It is also worth mentioning that the expected wide and dense
deployment of wireless sensors on the roads necessitates the
design of robust data aggregation techniques to deal with the
high redundancy and correlation of the transmitted informa-
tion, especially from neighboring sensors, as the redundant
transmission of this information may lead to quick depletion
of sensors battery, increase the delay of emergency messages,
as well as the collision rate. To reduce traffic data redundancy,
the optimal placement of wireless sensors on road networks
should be investigated and a trade-off solution between the
number of sensors deployed in a specific area, and road events
detection and accuracy should be designed. The spatial and
temporal correlation of traffic data are intrinsic characteristics
of road networks, which can be leveraged to solve both sensor
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data aggregation and optimal sensors placement problems in
future smart cities.
B. Machine to Machine (M2M) communication
A key technology that is a promising solution for reliable
and fast traffic data monitoring and collection is Machine to
Machine (M2M) communication. The M2M technology has
recently attracted increasing attention from both academic and
industrial researchers aiming to foster its application for data
collection in various environments. Recent forecasts [116],
[117] indicate an outstanding market growth over the next few
years for M2M devices usage and connectivity. According to
these forecasts, billions of devices will be potentially able to
benefit from the M2M technology. The report published by
the Organisation for Economic Co-operation and Development
(OECD) in [118] reveals that around 5 billion mobile wireless
devices are currently connected to mobile wireless sensor
networks, and foresees that this number will grow to reach
50 billion connected devices by the end of the decade. In
M2M communication, a sensor gathers traffic data and sends
it via wireless communication/cellular/3G/LTE networks to-
wards one or multiple central servers for processing purposes.
The ability of M2M devices to avoid the multi-hop trans-
mission, as opposed to WSNs, makes the data transmission
faster and more reliable, which represents a significant benefit
for the sensors reporting delay critical events. Moreover, it
is foreseeable that this technology will significantly enhance
the accuracy of data collection and lead to more flexible
deployment of sensors on the roads.
M2M over LTE networks is expected to be a key aspect
of future TMS. These M2M devices are equipped with access
technology capable of communicating in a reliable, fast and
extremely efficient way with the central entity that processes
and aggregates the collected data. Moreover, M2M solutions
support different classes of QoS, thus they can efficiently
collect prioritized data from multiple sources and ensure
that appropriate QoS is applied to each stream. The M2M
technology provides an extremely attractive solution for data
collection in urban areas due to its management benefits in
terms of reduced data reporting delay, high efficiency, and
low complexity. However, deploying M2M devices as an
alternative of WSNs technology will incur an additional cost
related to the use of cellular/3G/LTE networks. Therefore,
this may hinder the wide deployment of M2M technology
by city traffic managers, especially for cities with limited
financial resources, which is the case of the majority of cities
in developing countries.
C. Mobile sensing
In addition to the above data sources, mobile sensing
using mobile devices is expected to enable fast detection
of events on the roads and enhance the accuracy of traffic
conditions monitoring. According to recent studies in [32]
and [33], mobile crowd-sensing systems have been recently
used to provide more accurate real-time traffic information on
a large scale, using smart phones that enable services such
as, accurate localization of vehicles, faster and more precise
reporting of incidents and accurate travel time estimation for
improving commuters travel experience. The key enabler of
the widespread of mobile sensing applications, mainly for
traffic monitoring purposes, is the voluntary participation of
the users. These users demand high level of privacy, anonymity
and security guarantees in order to participate to such a system.
Indeed, these requirements constitute major concerns that need
to be carefully addressed to instigate larger participation of
mobile devices users to mobile sensing applications. These
issues can be dealt with as discussed in the following to
mitigate their impact on the TMS efficiency and accuracy of
its decisions.
• Trust management of mobile sensing data sources: how
to build a trust relationship with the mobile sensing data
source? In this case, reputation systems, such as [144],
need to be used to continually assess the level of trust-
worthiness of each mobile sensing data source. A mobile
data source is deemed trustworthy if the information it
has reported has been validated by either other mobile
sources or a trusted data source such as road-side sensors,
induction loops or CCTV cameras.
• Privacy preservation of mobile devices users: several
levels of privacy could be defined in the context of
smart cities, and users can adjust the setting of their
devices to increase/decrease the privacy level according,
for example, to traffic conditions (e.g. normal driving con-
ditions, traffic jam, incident ) and the service they need
to request from the TMS (e.g. optimal/fastest route to
their destination). Therefore, adaptive privacy protection
techniques that manage the users privacy preferences and
adapt the privacy level to the contextual factors in smart
cities are required.
• Design robust authentication techniques to prevent any
misuse of the system such as identity spoofing and fake
alerts, etc.
D. Social media
In the context of smart cities, social media feeds, such as
Twitter and Facebook for instance, can play an important
role in improving the accuracy and richness of the traffic
information provided by the traditional monitoring equipment
such as road sensors and induction loops. Despite the fact that
these pieces of equipment can measure the vehicles’ speed and
road segments’ occupancy to enable the estimation of traffic
congestion level, they are unable to identify the root event
that has led to this situation. [70] has shown that relying
on social media feeds, in addition to the traditional data
sources, can significantly enrich the real-time perception of
traffic conditions in the cities, and help to explain the reasons
behind the variation of the congestion level. Indeed, revealing
the real causes of the sudden increase of the congestion level
(e.g. accident, road works, political or social protest etc) will
enable more appropriate reaction from the road authorities
to alleviate the impact of this situation. Therefore, there is
a need to deeply investigate [71] this traffic data source to
enhance citizens’ quality of life and aid the traffic authorities
for efficient management of the increasing number of cars.
