Urban Traffic Simulators for Intelligent Transportation Systems

This paper describes the intelligent transportation systems technologies, methods and components and their application to traffic simulation and management. Examples of an agent-based intelligent transportation systems mesoscopic simulator and a cloud-based microscopic simulator are used to illustrate urban traffic management and incident response applications.

[1]  Ricardo García-Ródenas,et al.  An adaptive approach to enhanced traffic signal optimization by using soft-computing techniques , 2011, Expert Syst. Appl..

[2]  José Angel Olivas Varela,et al.  An adaptive approach to enhanced traffic signal optimization by using soft-computing techniques , 2011 .

[3]  Pravin Varaiya,et al.  Arterial travel time estimation based on vehicle re-identification using wireless magnetic sensors , 2009 .

[4]  Tim Edwards,et al.  Microscopic traffic simulation tool for Intelligent Transportation Systems , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[5]  Ardalan Vahidi,et al.  Predictive Cruise Control: Utilizing Upcoming Traffic Signal Information for Improving Fuel Economy and Reducing Trip Time , 2011, IEEE Transactions on Control Systems Technology.

[6]  Olivier C. L. Haas,et al.  Semantic agent-based controls for Service Oriented Architecture (SOA) enabled Intelligent Transportation Systems (ITS) , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

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

[8]  Zehang Sun,et al.  On-road vehicle detection: a review , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  David Gerónimo Gómez,et al.  Survey of Pedestrian Detection for Advanced Driver Assistance Systems , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Toby P. Breckon,et al.  Automatic Road Environment Classification , 2011, IEEE Transactions on Intelligent Transportation Systems.

[11]  Jon Bottom,et al.  Dynamic Traffic Assignment: A Primer , 2011 .

[12]  Jean-Yves Potvin,et al.  Vehicle routing and scheduling with dynamic travel times , 2006, Comput. Oper. Res..

[13]  J. Barceló.  Fundamentals of traffic simulation , 2010 .

[14]  Yong Zhao,et al.  Cloud Computing and Grid Computing 360-Degree Compared , 2008, GCE 2008.

[15]  Jaume Barceló,et al.  Models, Traffic Models, Simulation, and Traffic Simulation , 2010 .

[16]  Dirk Helbing,et al.  Self-control of traffic lights and vehicle flows in urban road networks , 2008, 0802.0403.

[17]  Haris N. Koutsopoulos,et al.  Dynamic data-driven local traffic state estimation and prediction , 2013 .

[18]  Keith J. Burnham,et al.  Key Learning Features as Means for Terrain Classification , 2013, ICSS.

[19]  Nick Hounsell,et al.  Review of urban traffic management and the impacts of new vehicle technologies , 2009 .

[20]  Tim Edwards,et al.  Cloud computing concept for Intelligent Transportation Systems , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[21]  René K. Boel,et al.  A Continuous Petri Net Approach for Model Predictive Control of Traffic Systems , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[22]  S. Kamran,et al.  A Multilevel Traffic Incidents Detection Approach: Identifying Traffic Patterns and Vehicle Behaviours using real-time GPS data , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[23]  Shian-Shyong Tseng,et al.  A knowledge based real-time travel time prediction system for urban network , 2009, Expert Syst. Appl..

[24]  Sherali Zeadally,et al.  Vehicular ad hoc networks (VANETS): status, results, and challenges , 2010, Telecommunication Systems.

[25]  Bo Chen,et al.  A Review of the Applications of Agent Technology in Traffic and Transportation Systems , 2010, IEEE Transactions on Intelligent Transportation Systems.