A Critical Review on the Development of Urban Traffic Models & Control Systems

Modeling and development of control systems to deal with the congestion at intersection in urban traffic is a critical research issue. Several approaches have been used to develop the modeling and controlling phenomenon in the said problem. These approaches include, 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.

[1]  Rahim F Benekohal,et al.  Lane assignment on automated highway systems , 1997 .

[2]  Agathoniki Trigoni,et al.  Efficient Data Propagation in Traffic-Monitoring Vehicular Networks , 2011, IEEE Transactions on Intelligent Transportation Systems.

[3]  Mariagrazia Dotoli,et al.  Validation of an Urban Traffic Network Model using Colored Timed Petri Nets , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[4]  Hong Dai,et al.  Application of Fuzzy Control in Combined Algorithm Between Traffic Guidance and Signal Timing , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[5]  Xi Yue,et al.  Intelligent Model of Urban Road Tunnel Ventilation System Based on Multi-Level Neural Network , 2009, 2009 Pacific-Asia Conference on Circuits, Communications and Systems.

[6]  Ziyou Gao,et al.  Control Strategies for Dispersing Incident-Based Traffic Jams in Two-Way Grid Networks , 2012, IEEE Transactions on Intelligent Transportation Systems.

[7]  Ruili Wang,et al.  Optimization of network traffic , 2009, 2009 IEEE International Conference on Control and Automation.

[8]  Hai-Jun Huang,et al.  Day-To-Day Route Choice Decision Simulation Based on Dynamic Feedback Information , 2010 .

[9]  Sascha Ossowski,et al.  A Computational Market for Distributed Control of Urban Road Traffic Systems , 2011, IEEE Transactions on Intelligent Transportation Systems.

[10]  Christophe Kolski,et al.  Modelling of an interactive system with an agent-based architecture using Petri nets, application of the method to the supervision of a transport system , 2006, Math. Comput. Simul..

[11]  Yi-Sheng Huang,et al.  A Timed Coloured Petri Net Supervisor for Urban Traffic Networks , 2006, The Proceedings of the Multiconference on "Computational Engineering in Systems Applications".

[12]  Zhiheng Li,et al.  A study on the framework of urban traffic control system , 2003, Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems.

[13]  Vincent Henn,et al.  Fuzzy route choice model for traffic assignment , 2000, Fuzzy Sets Syst..

[14]  Tao Zhang,et al.  Multi-agent System Model for Urban Traffic Simulation and Optimizing Based on Random Walk , 2010 .

[15]  Barbara Messing,et al.  An Introduction to MultiAgent Systems , 2002, Künstliche Intell..

[16]  Daniel Graupe,et al.  Principles of Artificial Neural Networks , 2018, Advanced Series in Circuits and Systems.

[17]  Wang Qing-chun,et al.  Notice of RetractionAn improved Dijkstra algorithm used on vehicle optimization route planning , 2010, 2010 2nd International Conference on Computer Engineering and Technology.

[18]  Integrating Fuzzy Mde-AT Framework For Urban Traffic Simulation , 2010 .

[19]  Yuanyuan Zhang,et al.  Research on Method of Double-Layers BP Neural Network in Prediction of Crossroads' Traffic Volume , 2009, ISNN.

[20]  Mariagrazia Dotoli,et al.  An urban traffic network model by first order hybrid Petri nets , 2008, 2008 IEEE International Conference on Systems, Man and Cybernetics.

[21]  Guo-Jiang Shen,et al.  Traffic Flow Modeling of Urban Expressway Using Artificial Neural Networks , 2006, ISNN.

[22]  Che-Fu Hsueh,et al.  A model and an algorithm for the dynamic user-optimal route choice problem , 1998 .

[23]  Larry Bull,et al.  Design of a Traffic Junction Controller Using Classifier Systems and Fuzzy Logic , 1999, Fuzzy Days.

[24]  Malika Singh,et al.  Preparation of Papers for International Journal of Scientific & Engineering Research , 2014 .

[25]  Dongbin Zhao,et al.  Computational Intelligence in Urban Traffic Signal Control: A Survey , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[26]  Stephen Graham Ritchie,et al.  TRANSPORTATION RESEARCH. PART C, EMERGING TECHNOLOGIES , 1993 .

[28]  Sun Youxian,et al.  Urban expressway traffic flow modeling and control using artificial neural networks , 2003, Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems.

[29]  MengChu Zhou,et al.  Petri nets and industrial applications: A tutorial , 1994, IEEE Trans. Ind. Electron..

[30]  Dipti Srinivasan,et al.  Computational intelligence-based congestion prediction for a dynamic urban street network , 2009, Neurocomputing.

[31]  Li Jun,et al.  Vehicle routing problem in dynamic urban traffic network , 2011, ICSSSM11.

[32]  Add Sadek,et al.  Self-Learning Intelligent Agents for Dynamic Traffic Routing on Transportation Networks , 2010 .

[33]  Zhu Quan,et al.  A traffic flow forecasting model based on BP neural network , 2009, 2009 2nd International Conference on Power Electronics and Intelligent Transportation System (PEITS).

[34]  Wang,et al.  Review of road traffic control strategies , 2003, Proceedings of the IEEE.

[35]  Xin Chen,et al.  A Multi-Agent Urban Traffic Control System Cooperated with Dynamic Route Guidance , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[36]  Yinglong Wang,et al.  Traffic congestion identification by combining PCA with higher-order Boltzmann machine , 2009, Neural Computing and Applications.

[37]  Dipti Srinivasan,et al.  Neural Networks for Real-Time Traffic Signal Control , 2006, IEEE Transactions on Intelligent Transportation Systems.

[38]  Andrzej Adamski,et al.  Situational Analysis in Real-Time Traffic Systems , 2011 .

[39]  Reyhaneh Maarefdoust,et al.  Traffic Modeling with Multi Agent Bayesian and Causal Networks and Performance Prediction for Changed Setting System , 2010, 2010 Second International Conference on Machine Learning and Computing.

[40]  Eleni I. Vlahogianni,et al.  Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach , 2005 .

[41]  William H. K. Lam,et al.  An activity-based time-dependent traffic assignment model , 2001 .

[42]  Manuel Silva Suárez,et al.  Hybrid Petri net model of a traffic intersection in an urban network , 2010, 2010 IEEE International Conference on Control Applications.

[43]  Mohammad Reza Meybodi,et al.  Formalized Learning Automata with Adaptive Fuzzy Coloured Petri Net an Application Specific to Managing Traffic Signals , 2022 .

[44]  Frank DiCesare,et al.  The Application of Petri Nets to the Modeling, Analysis and Control of Intelligent Urban Traffic Networks , 1994, Application and Theory of Petri Nets.

[45]  Chen Jun,et al.  Analysis on traffic congestion propagation influenced by traffic congestion information , 2011, 2011 International Conference on Electric Technology and Civil Engineering (ICETCE).

[46]  Andreas Hegyi,et al.  Model-based Control of Intelligent Traffic Networks , 2010 .

[47]  L. Yang.  Fuzzy Logic with Engineering Applications , 1999 .

[48]  Shiliang Sun,et al.  Neural network multitask learning for traffic flow forecasting , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[49]  Pei Liu.  A Neural Network Approach on Analyzing and Reducing Signalized Intersection Crashes , 2007, Third International Conference on Natural Computation (ICNC 2007).

[50]  Angela Di Febbraro,et al.  Urban traffic control structure based on hybrid Petri nets , 2004, IEEE Transactions on Intelligent Transportation Systems.

[51]  Dimitri Lefebvre,et al.  Continuous Petri nets models for the analysis of traffic urban networks , 2001, 2001 IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236).

[52]  Ziyou Gao,et al.  Urban traffic congestion propagation and bottleneck identification , 2008, Science in China Series F: Information Sciences.