Modelling and Analysis of Traffic Light Control Systems - Paper Review

Paper Review 1
MODELLING AND ANALYSIS OF TRAFFIC LIGHT CONTROL SYSTEMS
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Paper Review 2
Modelling and analysis of traffic light control systems
This article is a critique of the article entitled “Modelling and analysis of traffic light
control systems”, written by Huang, Y.S. and Su, P.J. and published in IET Control Theory
Applications Journal, volume 3, issue 3, pages 340-350. The article was published in 2009. The
main purpose of the authors was to design an urban traffic light control system comprising of
different phase duration. The authors developed the model using timed coloured Petri nets
(TCPN). Huang is a member of the Department of Electrical and Electronic Engineering at the
National defense University in Taiwan while Su is a member of the School of Defense Science at
the National Defense University also in Taiwan, Republic of China. The two authors have
conducted several studies related to development of transportation management systems hence
they have the necessary credentials to conduct a study and a write a useful research article in the
transportation engineering field. The article was also published in a journal that is dedicated to
publishing scientific or research articles about control systems and methods, including system
modelling, design and analysis of control systems, identification and simulation, and practical
implementation. The authors have also used a broad range of respectable and authentic sources
to support their claims. The sources include journal articles, books, conference proceedings,
reports and websites. Therefore this article is reliable for providing information that can be used
to improve efficiency and effectiveness of urban traffic light control systems.
The article has several parts including the abstract, introduction, definitions of TCPN,
basic traffic system description, modelling of traffic lights using TCPN, analysis of TCPN
models and conclusion. The authors have explained the need and importance of developing more
efficient traffic lights control systems to meet current traffic demands and the dominance of
using Petri nets for this purpose. The authors’ main purpose was to design a traffic light control

Paper Review 3
model using TCPN. The model would be used to improve control and management of traffic
congestion especially at intersections of urban road networks. The authors defined the basic
terms of TCPN and described the basic traffic system that they used in the study. This includes
the northbound and southbound (NS) vehicles and eastbound and westbound (EW) vehicles. The
TCPN model has also been defined by describing its various elements including traffic signals’
schedule (execution time, phase NS duration, phase EW duration and cycle time), signal
indication models, and signal timing plan model. The system developed has three TCPN models:
basic traffic system model and two extended models. The basic traffic system model comprises
of the NS traffic lights that describe the state of southern and northern traffic lights and EW
traffic lights that describe the state of the western and eastern traffic lights. The authors also
comprehensively described how the green, yellow and red lights of the traffic lights signals
controls on how the vehicles should move straight, turn left, turn right, prepare to move or stop
and wait.
The authors analyzed the TCPN model they designed using invariant method and
occurrence graphs method. However, the authors did not give reasons for choosing these two
methods and leaving out the third method that is also used in analyzing TCPN models –
simulation method. In invariant method, several equations were obtained from the TCPN model
created then the traffic control system’s performance was verified using these equations. The
three equations used to analyze the TCPN model using invariant method are (Huang & Su,
2009):
NS-G + NS-Y + NS-R + P1 + P2 + P3 + P4 = 1 ........................................... (1)
NS-G + NS-Y + NS-R + P1 + P2 + P3 + P4 = EW-R = 1 ................................. (2)

Paper Review 4
NS-G + NS-Y + EW-G + EW-Y + P1 + P2 + P3 + P4 + P5 + CP1 + CP2 = 1 .............. (3)
The occurrence graph method was used to ensure that the system model did not
experience any deadlock. The graphs were created by plotting a graph with a node for all
reachable markings plus an arc for all occurring binding elements. The analysis found that the no
dead-end nodes existed in the occurrence graph. The model was also tested in a real-world urban
traffic network by simulation using CPN tools. There were three types of intersections with a
total of five intersections in the real-world urban traffic network used. The intersections were
modelled, equation for each intersection formulated then used to verify the model. CPN tools
were used to simulate the results. The simulation results obtained were found to be the same and
consistent with the calculated or pre-determined results.
The number of vehicles in cities is continuously increasing faster putting a lot of pressure
on available traffic infrastructure (De Souza, et al., 2017). The continuous population growth in
most cities is also an indicator that available traffic infrastructure will continue being stressed.
This undermines quick mobility in cities yet it is among the most basic needs (Wang, et al.,
2015). It also compromises safety of drivers, passengers and pedestrians using roads in cities that
are always busy 24 hours a day. This makes the need to conduct research about effective and
efficient traffic light control systems imperative. Typically, traffic light control systems are
designed to ensure smooth and safe movement of vehicles at road intersections (Huang, et al.,
2017). Traffic congestion is caused by three main factors: events influencing traffic such as bad
weather conditions, working zones and incidents like accidents; traffic demand; and
transportation infrastructure that includes physical bottlenecks and traffic control devices
(Ghazal, et al., 2016). Most of the conventional traffic light control systems are not able to deal
effectively with the time fluctuating and complex traffic situations because they are developed