Intelligent Transport System Approach to Traffic Congestion Report

This report provides a comprehensive literature review on the application of Intelligent Transportation Systems (ITS) to address traffic congestion. It begins with an introduction to ITS, highlighting its use of information and communication technologies to improve transportation networks and enhanc...Read More

RUNNING HEAD: INTELLIGENT TRANSPORT SYSTEM APPROACH TO TRAFFIC CONGESTION 0
INTELLIGENT TRANSPORT SYSTEM APPROACH TO TRAFFIC CONGESTION

INTELLIGENT TRANSPORT SYSTEM APPROACH TO TRAFFIC CONGESTION 1
Table of Contents
Introduction......................................................................................................................................2
Literature review..............................................................................................................................2
Conclusion.......................................................................................................................................6
References........................................................................................................................................7

INTELLIGENT TRANSPORT SYSTEM APPROACH TO TRAFFIC CONGESTION 2

INTELLIGENT TRANSPORT SYSTEM APPROACH TO TRAFFIC CONGESTION 3
Introduction
Intelligent Transportation Systems is a combination of leading-edge information and
communication technologies used in transportation and traffic management systems to improve
the safety, efficiency, and sustainability of transportation networks, to reduce traffic
congestion and to enhance drivers' experiences (Perallos et al., 2015). It is an advanced
technology which offer innovative services and helps in monitoring as well as adjusting the road
network performance in real-time. In this report the literature review is done on “Intelligent
transport system approach to traffic congestion” to provide better understanding on the
Information Transportation system in various developed regions. The research problem includes
the high traffic loads, congestion traffic in the developed regions and the application of
Information Transportation system in urban region.
Literature review
Nellore and Hancke conducted the study to provide arrangement of different schemes for
managing the traffic and which can be used in avoiding the congestion (Nellore & Hancke,
2016). However, earlier schemes of urban traffic management regarding the prevention of
congestion. In this study research finds that there are various issues in managing the urban traffic
system that are traffic congestion, non- recurring congestion of traffic. The researcher also finds
that wireless sensing network can be adopted to detect the traffic. The study suggests that for
sensing the traffic, technologies like Radio- frequency identification, Inductive loop can be used.
In future the researcher can conduct the study by focusing on the variable like number of
accidents, violations in the traffic etc. According to Vallati et al development in the urbanization
increases the demand for transportation (Vallati et al., 2016). The study is conducted to know the
feasibility of mixed planning to handle the uncertain situations in controlling the urban traffic.
After, investigating researcher finds that earlier the fixed-time as well as reactive control is used
to control the traffic in urban countries. In study researcher finds that PDDL+ formulation is
considered the effective model in sensing the traffic rather than Fixed-time and reactive systems.
In future, the researcher can take the other actions of controlling the traffic that inconsistent limit
of speed. According to Yun and Qu freeways zones of work leads traffic blockages, which

INTELLIGENT TRANSPORT SYSTEM APPROACH TO TRAFFIC CONGESTION 4
results in series of troubles, variation in high-speed, dissatisfaction of the driver as well as traffic
congestion (Yun & Qu, 2018). The study is conducted to develop a This research aims to
develop connected as well as automated vehicles collaborative component to improve the
harmful effects, which is cause by work zones. For the investigation the model of cellular
automata is used to analyze the weather automated as well as connected vehicles helps in
enhancing the operations of traffic. The researcher finds that standard performances of traveling
time, safety as well as emission all could be enhanced and stochasticity of performances will also
be reduced.
Zhang et al conducted the study to recognize the major issues in development of the systems
which can be used in managing and controlling the agent-based traffic (Zhang et al., 2018). For
collecting the information various articles are reviewed in which approach of agent-based is
already reports. The researcher believes that main challenge which the roadways transportation
faces are accidents, delays in transportation, vehicle emissions, and traffic congestion. The
researcher finds that model of urban traffic control, intersection signal control, the system of
route-guidance can be used to manage and control the urban traffic system. Lv et al conducted
the study to know the traffic flow, in the developed countries, in this researcher used the
prediction method as well as model of stacked auto encoder to recognize the features of traffic
flow (Lv et al., 2015). In this study various articles were reviewed, for the data collection Styled
AutoEncoder model is used. The researcher finds that the Support Vector Machine as well as
Radial Basis Function NN model is considered better to control and manage the traffic in
developed countries. In future the researcher can adopted the algorithms of deep learning to
investigate the effectiveness of traffic flow. According to Wan et al advancement in the
techniques of wireless communication, automotive as well as technology of intelligent terminal
are driving the progression of automobile ad hoc network into the vehicles Internet (Wan et al.,
2016). It results in alteration in the vehicle problems in direction finding which is based on
calculation towards prediction of real time traffic. The study is conducted to find the relationship
between clouds computing as well as internet of vehicles. After, investigating researcher finds
that technology of mobile crowd sensing can be adopted to support the choices of dynamic
routes and to avoid as well as avoid the congestion.

INTELLIGENT TRANSPORT SYSTEM APPROACH TO TRAFFIC CONGESTION 5
Simone et al conducted the study to investigate the performance of the strategy of traffic-
responsive (Simone et al., 2017). It is used in controlling the parameters of traffic light in the
urban network to decrease the traffic congestion. The researcher believe that in controlling the
urban traffic two major qualities should be keep in mind that is cycle time as well as split time.
Te researcher finds that model of mesoscopic simulation can be adopted to check the
performance of future in the action of control. In future researcher can concentrate more on
elaborating the strategies of cycle time control. Sobral et al conducted the study by proposing
the holistic method for the classification of highway traffic video which is based on the
properties of crowded vehicle (Sobral et al., 2013). This method does the classification of traffic
congestion in the three areas that is heavy, medium as well as light. The classification is done by
using the density of average crowd as well as speed of the crowd. In this study researcher
estimated the crowd density which is analyzed by background subtraction as well as speed of
crowd is analyzed by pyramidal Kanade Lucas Tomasi tracker. This classification of features
with the network of neural demonstrates that 94.50% of the accurateness on experiment derived
from the 254 traffic videos of UCSD data set.
Milojevic and Rakocevic conducted the study to present the designed algorithm which allows in
detecting all the vehicles for sensing the level of traffic congestion in the whole scattered path
(Milojevic & Rakocevic, 2014). This also helps in providing the supporting information that is
data of the traffic from the authorities. After observing the traffic congestion of each vehicle and
by adjusting the broadcast it provides the information about the traffic. This also makes entire
vehicle alert regarding the level of congestion on the road. This algorithm works when ten
percent of vehicles, which all are in the network, are enabled by Vehicular ad-hoc network.
However broadcasting of data usually used to make sure that vehicle does not provide
unnecessary data regarding the traffic congestion. Souza et al aim of the study is to suggest the
traffic system which is focused on the communication among the vehicles to avoid the traffic
congestion (Souza et al., 2014). The purpose is getting the solution in reducing the CO2
emissions, trip average time as well as fuel consumption by ignoring the congested streets. This
intelligent system decrease the timing of the trip by eighty five percent, consuming fuel by forty
percent as well as Co2 emissions by fifty five percent. Al-Sakran conduct the study to present an
administration of traffic system, the system is based on the intelligent Internet things which are
featured by lower cost, easiness in upgrading (Al-Sakran, 2015). The intelligent information

INTELLIGENT TRANSPORT SYSTEM APPROACH TO TRAFFIC CONGESTION 6
system is majorly based on technology detection, network sensing. This helps in managing,
monitoring the traffic automatically. The researcher suggests using the radio frequency
identification technology for tracking the traffic.
Zhao et al objective of the study is to explain the short- range prediction of traffic because it is
the major problem in the transportation intelligent system (Zhao et al., 2017). The adequate
prediction enable the travelers in developing the adequate travel modes, travel routes as well as
departure time, which is considered significant for managing as well as tracking traffic. This
helps in providing the information about the traffic to enhance the accuracy of forecasting. The
researcher also proposes the novel forecast model because it is based on both long as well as
short range memory network. Barmpounakis, Vlahogianni and Golias believe that two wheelers
powered penetration in the urban areas has significant effect on the conditions of the traffic
(Barmpounakis et al., 2015). In this study the intelligent transportation system is analyzed with
the perspective of microscopic as well as macroscopic by focusing on two wheeler powered
penetration to know their effects on the environment. The researcher finds that the traffic can be
improved by optimizing the interactions of microscopic. Zhang et al objective of the study is to
demonstrate the hierarchical system development which is based on the rule of fuzzy and
optimize by inherent algorithms for the development of the proper traffic congestion prediction
system (Zhang et al., 2014). This system reduces the size of variables as well as maintains the
high level of accuracy. The system also tested on the problems of traffic congestion and also on
the benchmark data. In this study the results are compared with the literature algorithms for
confirming the effectiveness of approach.
Souza et al believe that congestion is considered the major problem in the big cities; it is caused
due to the increasing traffic in the peak hours (de Souza et al., 2016). Researcher also believes
that solution is to perceive the conditions of traffic, but this approach does not considered the
long term solutions to the problem of congestion. For this problem researcher proposed the
intelligent traffic system that is CHIMERA, this helps in enhancing the entire road network
utilization as well as decreases the costs of travelling by ignoring the vehicles from getting
jammed in the traffic. According to Fernandez et al systems of intelligent transportation is
considered the technological solutions which help in enhancing the performance as well as
providing security and safety in the transportation of roads (Fernandez et al., 2016). Researcher

INTELLIGENT TRANSPORT SYSTEM APPROACH TO TRAFFIC CONGESTION 7
believes that system can only be successes when the information is exchanged among the
components in the infrastructure of roads, for this significant source is sensor. This helps in
providing information about the weather, situation of the traffic and enhances the process of
driving. Researcher suggests ontology driven architecture to enhance the environment of driving.
Anisi and Abdullah believe that intelligent transportation have enhanced the system of
transportation and provide the information of traffic to all travelers (Anisi & Abdullah, 2016). In
this study wireless sensor networks is replaced with the wired sensors as well as systems of
traffic monitoring. Researcher proposes the two tier architecture, in which it includes the mobile
object network; this approach helps in transferring the objects of mobile. For providing the
accurate information of traffic a cost function of QoS- aware link were also proposed by the
researcher. The results shows that the architecture effectiveness as well as mechanism of data
reporting is used in the applications of ITS.
Zhong et al formulated the model of modified cell transmission to control and manage the traffic
congestion in the urban areas (Zhong et al., 2016). Researcher finds that earlier system of linear
complementarity is used to manage the high traffic congestion, from the investigation researcher
also finds that linear complementarity model can be converted into modified cell transmission.
The formulation of the designing of approach leads suitability in estimating the traffic state.
Chen et al conducted the study to examined that the effects of traffic rule in the performance of
the traffic system in urban network (Chen et al., 2018). In this study researcher finds that the
performance can be improved if the rule of anticlockwise is combine with the strategy of
feedback which is based on the route guidance. Hoogendoorn, Arerm and Hoogendoom
objective of the study is explain about the automation, which is considered as an influence on the
flow of traffic efficiency (Hoogendoorn et al., 2014). The study also focused on the behavior of
road user as well as formulation of theory structure. In this researcher recognize the needs of
research for future, after investigation researcher finds that automation has higher impact on the
efficiency of traffic flow as well as behavior of the road user. The study also has various
limitations and it provides directions for the future which is related to the efficiency of traffic
flow.

INTELLIGENT TRANSPORT SYSTEM APPROACH TO TRAFFIC CONGESTION 8
Conclusion
From the above, it finds that Intelligent Transportation Systems is considered the information as
well as communication technology, which helps in enhancing the safety and also help in
reducing the traffic congestion. In this report various articles related to the problem of high
traffic congestion in the urban areas. From the above literature reviewed, it finds that traffic
system that is CHIMERA as well as Vehicular ad-hoc network can be used for managing as well
as preventing the traffic in effective manner.

INTELLIGENT TRANSPORT SYSTEM APPROACH TO TRAFFIC CONGESTION 9
References
Al-Sakran, H.O., 2015. Intelligent traffic information system based on integration of Internet of
Things and Agent technology. International Journal of Advanced Computer Science and
Applications (IJACSA), 6(2), pp.37-43.
Anisi, M.H. & Abdullah, A.H., 2016. Efficient data reporting in intelligent transportation
systems. Networks and Spatial Economics, 16(2), pp.623-42.
Barmpounakis, E., Vlahogianni, E.I. & Golias, J.C., 2015. Intelligent transportation systems and
powered two wheelers traffic. IEEE Transactions on Intelligent Transportation Systems, 17(4),
pp.908-16.
Chen, B. et al., 2018. Effects of traffic lights for Manhattan-like urban traffic network
inintelligent transportation systems. Transportmetrica B: Transport Dynamics, 6(1), pp.4-16.
de Souza, A.M. et al., 2016. Real-time path planning to prevent traffic jam through an intelligent
transportation system. In 2016 IEEE Symposium on Computers and Communication (ISCC),
pp.726-31.
Fernandez, S. et al., 2016. Ontology-based architecture for intelligent transportation systems
using a traffic sensor network. Sensors, 16(8).
Hoogendoorn, R., Arerm, B.v. & Hoogendoom, S., 2014. Automated driving, traffic flow
efficiency, and human factors: Literature review. Transportation Research Record, 2422(1),
pp.113-20.
Lv, Y. et al., 2015. Traffic flow prediction with big data: a deep learning approach. IEEE
Transactions on Intelligent Transportation Systems, 16(2), pp.865-73.
Milojevic, M. & Rakocevic, V., 2014. Distributed road traffic congestion quantification using
cooperative VANETs. In 2014 13th Annual Mediterranean Ad Hoc Networking Workshop
(MED-HOC-NET), pp.203-10.

INTELLIGENT TRANSPORT SYSTEM APPROACH TO TRAFFIC CONGESTION 10
Nellore, K. & Hancke, G., 2016. A survey on urban traffic management system using wireless
sensor networks. Sensors, 16(2).
Perallos, A. et al., 2015. Intelligent Transport Systems: Technologies and Applications.
Hoboken: John Wiley & Sons.
Simone, B. et al., 2017. A simulation-based traffic signal control for congested urban traffic
networks. Transportation Science, 53(1), pp.6-20.
Sobral, A., Oliveira, L., Schnitman, L. & Souza, F.D., 2013. Highway traffic congestion
classification using holistic properties. In 10th IASTED International Conference on Signal
Processing, Pattern Recognition and Applications.
Souza, A.M.d. et al., 2014. Decreasing greenhouse emissions through an intelligent traffic
information system based on inter-vehicle communication. In Proceedings of the 12th ACM
international symposium on Mobility management and wireless access, pp.91-98.
Vallati, M. et al., 2016. Efficient macroscopic urban traffic models for reducing congestion: a
PDDL+ planning approach. In Thirtieth AAAI Conference on Artificial Intelligence.
Wan, J. et al., 2016. Mobile crowd sensing for traffic prediction in internet of vehicles. Sensors,
16(1).
Yun, Z. & Qu, X., 2018. On the impact of connected automated vehicles in freeway work zones:
a cooperative cellular automata model based approach. Journal of Intelligent and Connected
Vehicles, 1(1), pp.1-14.
Zhang, S. et al., 2018. Vehicular communication networks in the automated driving era. IEEE
Communications Magazine, 56(9), pp.26-32.
Zhang, X. et al., 2014. Hierarchical fuzzy rule-based system optimized with genetic algorithms
for short term traffic congestion prediction. Transportation Research Part C: Emerging
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Zhao, Z. et al., 2017. LSTM network: a deep learning approach for short-term traffic forecast.
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INTELLIGENT TRANSPORT SYSTEM APPROACH TO TRAFFIC CONGESTION 11
Zhong, R.X. et al., 2016. Linear complementarity system approach to macroscopic freeway
traffic modelling: uniqueness and convexity. Transportmetrica A: Transport Science, 12(2),
pp.142-74.