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A Critical Review on the Development of Urban

Traffic Models & Control Systems

Rishi Asthana, Neelu Jyoti Ahuja, Manuj Darbari, Praveen Kumar Shukla

AbstractModeling and development of control systems to deal with the congestion at intersection in urban traffic is a critical research is- sue. Several approaches have been used to develop the modeling and controlling phenomenon in the said problem. These approaches in- clude, Petri net, Fuzzy Logic, Neural Network, Genetic Algorithms, Activity Theory, Multi Agent Systems and many more. This paper is a survey on the development of Urban Traffic Control Systems using techniques discussed above in the last decade.

Index TermsFuzzy Logic, Neural Network, Genetic Algorithms, Multi Agent Systems, Activity Theory, Petri Nets.


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he development of control systems to deal with the con- gestion at the intersection in urban traffic is a critical re- search issue. The prime requirements of the developed system are, the signal must not allow the ambiguous move- ment to the traffic and it must be clear that how/when the indication of signal shown to be changed. Two other aspects to be handled are to take decisions about signal indication se- quence in the control system to make the system well opti-
mized and development of control logic for signal generation.
This paper has been divided into 6 subsections. In section 2,
Petri net based modeling has been studied and revised.
Section 3 contains the review of multi-agent systems in urban
traffic control systems. Neural Network based approaches are
discussed in Section 4. Section 5 contains the fuzzy logic based
approaches in Traffic Control Systems. Several hybrid
approaches of fuzzy logic, neural network, petri nets are
discussed in section 6. Section 7 contains various other
approaches for the traffic control systems, like activity theory,
complex network theory, incident and real time traffic control


Petri Nets [1] are also known as a place/Transition Net or
P/T net. It is the mathematical modeling language for the de-


Rishi Asthana is working as Assistant Professor with the Department of Electrical Engineering in Babu Banarsi Das National Institute of Technol- ogy and Management,Lucknow, India

Neelu Jyoti Ahuja is working as Assistant Professor with the Department of Computer Science in UPES, Dehradun, India E-

Manuj Darbari is working as Associate Professor with the Department of InfomationTechnology in Babu Banarsi Das National Institute of Technol- ogy and Management,Lucknow, India E-mail:

Praveen Kumar Shukla is pursuing Ph.D. in Compter Science from Gau- tam Buddh Technical University,Lucknow, India and he is also working as

a facultymember in the department of Information Technology in North- ern India Engineering College,Lucknow,India.E-
scription of Discrete Event Systems (DES). PN theory is devel- oped in 1962 by Carie Asam Petri. These are highly applicable in graphical modeling, Mathematical modeling, simulation and real time control by the use of places and transitions.
Different variations of the Petri Nets are applied in the modeling and control of traffic systems.
A Colored Timed Petri net (CTPN) model has been used for validating a Urban Traffic Network in [2].
A model for real time control of urban traffic networks is proposed in [3]. A modular framework based on first order hybrid Petri nets model is developed. The vehicle flows by a first order fluid approximation, in this approach. The lane in- terruptions and the signal timing plan controlling the area are developed by the discrete event dynamics integrated with timed Petri nets.
A new hybrid Petri net model for modeling the traffic behavior at intersection is developed in [4]. The im- portant aspects of the flow dynamics in urban networks are interpreted very well.
A new approach of continuous Petri nets with variable speed (VCPN) is proposed in [5]. The analysis and control design in urban and interurban networks is done.
A network model via hybrid Petri nets [6] is used to dem-
onstrate and implement the solution of the problem of coordi-
nating several traffic lights. It aims the improvement in the
performance of some classes of special vehicles, like public
and emergency vehicles.
A model of TCPN (Timed Control Petri Nets) is used to
demonstrate and solve the problem of coordinating sever-
al traffic lights in [7]. The analysis of the control TCPN mod-
els is done by Occurrence Graphs (OG) techniques.
A Colored Petri Net Model of an urban traffic network for
the purpose of performance evaluation is demonstrated in [8].
The subnets for the network, the intersections, the external
traffic inputs and control are discussed.
A Urban Traffic Simulation has been done using petri net in
[9]. This approach is based on generating producer consumer
network and grid simulation of petri nets.

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A multi-agent system (MAS) [10] is a system consists of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems that are possible to be difficult or impossible for an individual agent or a monolithic sys-
al and algorithmic searching, finding and processing techniqu es.
A multi agent system approach to develop distributed un- supervised traffic responsive signal control models, has been developed in [11]. Each agent in the system is a lo- cal traffic signal controller for one intersection in the traffic network. The first multi agent system is identified using hybrid soft computing techniques. Each agent employs a multistage online learning process to update and adapt its knowledge base and decision-making procedure. The second multi agent system is produced by integrating the simultane- ous perturbation stochastic approximation theorem in fuzzy extended neural networks (NN).
An approach to model the traffic of an important crossroad in Mashhad city using intelligent elements in a multi-agent environment and a large amount of real data, has been devel- oped in [12]. The overall traffic behavior at the intersection was first modeled by the Bayesian networks structures. Also, the probabilistic causal networks are used to model the effec- tive factors.
Among the several ITS applications is the notion of Dynam-
ic Traffic Routing (DTR), which involves generating “optimal”
routing recommendations to drivers with the aim of maximiz-
ing network utilizing. In [13], it has been presented that the
feasibility of using a self-learning intelligent agent to solve the
DTR problem to achieve traffic user equilibrium in a transpor-
tation network. The agent then learns by itself by interacting
with the simulation model. Once the agent reaches a satisfac-
tory level of performance, it can then be deployed to the real-
world, where it would continue to learn how to refine its con-
trol policies over time.
The integration of cooperative, distributed multi-agent sys-
tem to improve urban traffic control system is proposed in
[14]. Real-time control over the urban traffic network is done
through an agent-based distributed hierarchy traffic control
system. This system cooperates with dynamic route guidance
system. Cooperative system framework and agent structure
are discussed in this work.
A new framework of hybrid control system for UTC is pre-
sented in [15], in which any optimal control strategy can be
adopted. By the interface D-S and interface C-S namely coop-
eration model, the hybrid system of UTC is divided into three
layers including digital control loop, discrete event module,
and Group Decision-making Support System (GDSS). By inte-
grating GDSS consisted of central agent and intersection
agents, real time control and coordinate control with the cha-
racteristics of self-decision, cooperation, and intelligence are
An approach of modeling the urban traffic flow system is
discovered for combining the global and local model informa-
tion for the whole city net in [16]. It is assumed that traffic di-
graph consists of several nodes and those nodes are linked with routes lines. The proposed system uses the random walk theory. Vehicle flow density and driver strategy independence are also the important factors in this approach.
An agent-based approach to model the individual driver
behavior under the influence of real-time traffic information is
proposed in [17]. The driver behaviour models developed in
this work are based on a behavioural survey of drivers. This
survey was conducted on a congested commuting corridor in
Brisbane, Australia. Based on the results obtained from the
behavioural survey, the agent behaviour parameters which
define driver characteristics, knowledge and preferences were
identified and their values determined.


An artificial neuron is a computational model inspired in the natural neurons. Natural neurons receive signals through synapses located on the dendrites or membrane of the neuron. When the signals received are strong enough (surpass a cer- tain threshold), the neuron is activated and emits a signal though the axon. This signal might be sent to another synapse, and might activate other neurons. The network developed on this theory in called Artificial Neural Network (ANN) [18]. The ANNs are highly applicable in the design of models for traffic control systems.
An intelligent model consists of two levels of neur- al network [19] for the traffic control system. The first level is a traffic flow neural network model to predict the traffic flow changes in road tunnel, the result of predicting will be used as an input of the second level neural network which is used to describe an intelligent model of urban road ventilation system, through the different states of predicted traffic flow, to estab- lish an intelligent model of urban road tunnel ventilation sys- tem based on multi-level neural network.
A neural network model is proposed for forecasting crossroads traffic flow using back propagation (BP) neural network in [20]. The work gives a new reliable and effective way of forecasting short term traffic flow of crossroads
in urban road network.
A commonly used macroscopic dynamic deterministic traffic flow model for traffic control is analyzed in [21]. The neural network model for the urban expressway traffic flow is established and the urban expressway multi-variable neural control strategy with both the on-ramp control and the road speeds control is implemented.
MTL (Multi Task Learning) based neural networks are used for traffic flow forecasting in [22]. For neural network MTL, a back propagation (BP) network is discovered by incorporating traffic flows at several contiguous time instants into an output layer. Nodes in the output layer can be seen as outputs of different but closely related STL tasks.
A back propagation artificial neural network model, which utilizes the characteristics of urban signalized intersec- tions for occurrence prediction of intersection - re- lated traffic crashes, along with its application for crash reduc-

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tion, are proposed in [23]. With the ANN model, a proposed decision-making scheme for intersection rehabilitation was suggested.
A commonly used macroscopic dynamic deterministic traf- fic flow model is analyzed in [24]. The 1.5-layer feed-forward network modeling for the urban expressway traffic flow is implemented.
A novel intelligent identification method is proposed in [25] to reduce the computation cost and to improve the identifica- tion rate. The proposed method combines principal compo- nent analysis (PCA) method with higher-order Boltzmann machine (BM). PCA is applied to reduce the dimension of in- put feature space. It can not only reduce the computation cost but also filter noise of the source data. BM is a kind of stochas- tic network that is used to get the global optimum solution. Higher-order BM without hidden units can save lots of com- putation cost without decreasing modeling power. The trained higher-order BM is used to identify traffic state.
Short-term forecasting of traffic parameters such as flow and occupancy is an essential element of modern Intelligent Transportation Systems research and practice. An advanced, genetic algorithm based, multilayered structural optimization strategy that can help both in the proper representation of traffic flow data with temporal and spatial characteristics is presented in [26]. After that, it evaluates the performance of the developed network by applying it to both univariate and multivariate traffic flow data from an urban signalized arteri- al.


Fuzzy logic [27] is a form of many-valued logic; it deals with reasoning that is approximate rather than fixed and ex- act. In contrast with traditional logic theory, where binary sets have two-valued logic: true or false, fuzzy logic variables may have a truth value that ranges in degree between 0 and 1. Fuzzy Logic Theory is highly applicable in the design strate- gies for modeling traffic control systems.
The coordination of Urban Traffic Flow Guidance System (UTFGS) and Urban Traffic Control System (UTCS) can give decision support to navigation and signal timing at the same time. It realizes basic information sharing and to get the op- timal results from the point of system integration. A com- bined model of traffic assignment and signal control is pre- sented in [28], with the object to minimize congestion degree both at links and intersections. To avoid the complexity and difficulties in solving the optimal model, a fuzzy control algo- rithm is put forward, the input collected traffic data is de- scribed. Then the fuzzy control rules are listed in table to get the optimal link volumes.
The automated urban traffic control systems [29] are based on deterministic algorithms. They have a multi-level architec- ture. To achieve global optimality, hierarchical control algo- rithms are generally employed. An alternative approach is to use a fully distributed architecture in which there is effectively only one (low) level of control. These systems are aimed at increasing the response time of the controller and, again, these often incorporate computational intelligence techniques.
A new route choice model taking account of the impreci- sion and the uncertainties lying in the dynamic choice process is proposed in [30]. This model makes possible a more accu- rate description of the process than those (deterministic or stochastic) used in the literature. It is assumed that drivers choose a path all the more than it is foreseen to have a lesser cost. The predicted cost for each path is modeled by a fuzzy subset which can represent imprecision on network know- ledge (e.g. length of links) as well as uncertainty on traffic conditions (e.g. congested or uncongested network, incident).


Sometimes, the approaches like, neural network fuzzy log- ic, petri net are hybridized to develop the model for urban traffic control systems.
A hybrid model for single point short term traffic flow fore- casting in an urban traffic network is proposed in [31]. The hybrid model consists of two main modules: a fuzzy input fuzzy output filter (FIFO-filter) and a multi-layer feed-forward artificial neural network architecture optimized using evolu- tion strategies (MLFN-ES). The FIFO-filter does the data clus- tering operation and results in a rough forecasted prediction value based on the input data to the MLFN-ES associated with each cluster for modeling the input–output relation to provide accurate short term forecast value.
A hybrid adaptive model, based on a combination of co- lored Petri nets, fuzzy logic and learning automata has been studied in [32].
An original method using high level petri nets for the speci- fication and design of interactive systems is presented in [33]. An agent oriented architecture based on the classic compo- nents of an interactive application (application, dialogue con- trol, and interface with the application) is demonstrated.
An approach of intelligent urban traffic control is devel- oped in [34], using the neuro-genetic petri net approach. In this approach genetic algorithm is used to provide dynamic change of weight for faster learning and converging of neuro- petri nets.


7.1 Incident based traffic jam

Effective control strategies are required to disperse incident based traffic jams in urban networks. In [35], such a control strategy has been developed and their effectiveness in dispers- ing incident-based traffic jams in two-way rectangular grid networks is presented. The spatial topology of traffic jam is proposed for propagation, the concept of vehicle movement ban is implemented, which is frequently adopted in real urban networks as a temporary traffic management measure.

7.2 Real time Traffic Control

A vehicle routing problem in dynam- ic urban traffic network with real-time traffic information is presented in [36]. Both re-current and non- recurrent congestion are handled in the problem. A method to

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solve the problem by combining the initial routes arrangement with the real-time route adjustment has been implemented. The genetic algorithm is also integrated in this work.
The prediction of traffic situations is a vital issue in mod- ern Intelligent Transport Systems (ITS). A situational algo- rithm of real time traffic is proposed in [37].

7.3 Survey Work

Urban transportation system consists of surface-way networks, freeway networks, and ramps with a mixed traffic flow of vehicles, bicycles, and pedestrians. In [38], a survey has been carried out for control and management of recurrent and non-recurrent congestion in traffic network using computational intelligence techniques.

7.4 Optimization Complex Network Theory

The three factors are important for tuning the network traf- fic system: (i) the topology of underlying infrastructure; (ii) the distribution of traffic resources; (iii) the routing strategy. The optimization of network capacity based on com- plex network theory is done in [39]. The optimization method of network traffic in several situations corresponding to the real cases has been studied, also.

7.5 Dijkestra Algorithm

Route network model, construction of route network database and optimization route algorithm has been studied in [40]. Theurban route network model, which includes direction, crossing delay and restraint of urban traffic is introduced. The resolution to optimization route of turning delay and restraint is presented based on im- proved Dijkstra algorithm and programs the algorithm.

7.6 Model Based

Some advanced model-based control methods for intelli- gent traffic networks are discussed in [41]. Specifically, we consider model predictive control (MPC) of integrated free- way and urban traffic networks. The basic principles of MPC are presented for traffic control including prediction models, control objectives, and constraints. The proposed MPC control approach is modular, allowing the easy substitution of predic- tion models and the addition of extra control measures or the extension of the network.

7.7 Other developments for UTCS approaches

The mathematical model was formulated in [42] to describe the effectiveness of traffic jams information under the assump- tion of simple network and linear cost function. The impact of traffic congestion information on congestion propagation was discovered by using two models namely stochastic and deterministic user equilibrium assignment.
In [43], the problem of efficiently collecting and disseminat- ing traffic information in an urban setting is discovered. The traffic data acquisition problem and explore solutions in the mobile sensor network domain while considering realistic application requirements is formulated.
In the last decade, economic approaches based on computa- tional markets have been proposed as a paradigm for the de-
sign and control of complex socio-technical systems, such as urban road traffic systems. The control problem of an urban road traffic system can be modeled as a distributed resource-allocation problem to apply market-based techniques as solution methods. A competitive computational market is designed in [44], where driver agents trade the use of the ca- pacity inside the intersections with intersection manager agents.
Traffic congestion in urban road and freeway networks leads to a strong degradation of the network infrastructure and accordingly reduced throughput, which can be countered via suitable control measures and strategies. A comprehensive overview of proposed and implemented control strategies is provided for three areas: urban road networks, freeway net- works, and route guidance has been discussed in [45].
The automation of highways as part of the intelligent ve- hicle highway system (IVHS) program is seen as a way to alle- viate congestion on urban highways. The concept of lane as- signment in the context of automated highway systems (AHS) is discussed in [46]. Lane assignments represent the schedul- ing of the path taken by the vehicle once it enters an auto- mated multilane corridor. The classification of lane assign- ment strategies is developed into non-partitioned (totally un- constrained, general, and constant lane) and partitioned (des- tination monotone, origin monotone, and monotone) strate- gies. An optimization problem is also formulated with the performance criterion being a combination of travel time and manoeuvre costs.
A congestion propagation model of urban network traffic is
proposed in [47] based on the cell transmission model (CTM).
The proposed model includes a link model, which describes
flow propagation on links, and a node model, which
represents link-to-link flow propagation. A new method of
estimating average journey velocity (AJV) of both link and
network is developed to identify network congestion bottle-
necks. A numerical example is studied in Sioux Falls urban
traffic network.
Intelligent transportation systems (ITS) is effective on solv-
ing the problem of traffic jam in cities. Prediction of cros-
sroads’ traffic volume is the key technology in ITS. In [48], BP
neural network is universally used in prediction of crossroads’
traffic volume.
In reality, the individual's day-to-day route choice behavior
is a long-time evolution process, and travelers choose their
traveling routes according to the combination of historical
experience and real-time traffic information. Considering two
classes of users, one equipped with advanced traveler infor-
mation systems (ATIS) and the other without, the travel effi-
ciency under two different information feedback strategies,
namely, travel time feedback strategy and mean velocity feed-
back strategy, has been investigated in [49].
A discrete-time, link-based dynamic user-optimal route
choice problem using the variational inequality approach is
formulated in [50]. The proposed model complies with the
dynamic user-optimal equilibrium condition in which for each
origin-destination pair, the actual travel time experienced by
travelers departing during the same interval is equal and mi-

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7.8 Activity Theory Based

Activity Theory is more of a descriptive meta-theory or framework than a predictive theory. It considers entire work/activity system that includes teams, organizations, etc., beyond just one actor or user and accounts for environment, history of the person, culture, role of the artifact, motivations, complexity of real life action, etc.

A conceptual activity-based and time-dependent traffic as- signment model is proposed in [51]. The temporal utility pro- files of activities are employed to formulate the temporal activ- ity choice behavior of individuals as a multinomial logic mod- el. Route choice behavior is then described as the ideal dynam- ic user equilibrium condition.
A model for urban traffic control has been proposed in [52],
which integrates the model driven engineering and activity
theory. It is also extended with fuzzy logic to deal with issues
of uncertainty.

Conclusion & Future Scope

The development of models and control systems for Urban Traffic is an important research issue. Several problems and research issues have been identified in this field. To deal with these issues, several approaches and models have been pro- posed and implemented using Fuzzy Logic, Neural Network, Petri Net, etc. These approaches have been reviewed in this paper.
In future, the authors are interested in the development of urban traffic control systems using satellite based and global positioning systems.


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