Green wave-based virtual traffic light management scheme with VANETs

Green intelligent transportation systems GITS have received significant attention in recent years. Traffic congestion, which is an important research topic on intelligent transportation systems ITS, is rapidly becoming one of the most serious problems affecting urban areas. This study utilises the vehicular ad hoc networks VANETs technology to build a virtual traffic light VTL environment to reduce traffic congestion. The objective of this study is to adjust the traffic light signal time length on-demand to enable vehicles to pass through numerous intersections rapidly, thereby saving energy and reducing fuel consumption. The traffic phase combination algorithm and VTL operation mechanism are also addressed to improve the traffic congestion situation in urban areas. Simulation results indicate that the proposed scheme can increase the percentage of traffic flow and the average speed of all vehicles in all intersections, thereby saving energy.

[1]  Jian Sun,et al.  Development and simulation application of a dynamic speed dynamic signal strategy for arterial traffic management , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[2]  Zhang Yi,et al.  Multiobjective Reinforcement Learning for Traffic Signal Control Using Vehicular Ad Hoc Network , 2010, EURASIP J. Adv. Signal Process..

[3]  Tein-Yaw Chung,et al.  Adaptive Momentum-Based Motion Detection Approach and Its Application on Handoff in Wireless Networks , 2009, Sensors.

[4]  Jesper Larsen,et al.  Green Wave Traffic Optimization - A Survey , 2008 .

[5]  Hannes Hartenstein,et al.  The impact of traffic-light-to-vehicle communication on fuel consumption and emissions , 2010, 2010 Internet of Things (IOT).

[6]  Hejun Wu,et al.  Adaptive Traffic Light Control of Multiple Intersections in WSN-Based ITS , 2011, 2011 IEEE 73rd Vehicular Technology Conference (VTC Spring).

[7]  Ozan K. Tonguz,et al.  Self-organized traffic control , 2010, VANET '10.

[8]  Li-Der Chou,et al.  ADAPTIVE VIRTUAL TRAFFIC LIGHT BASED ON VANETS FOR MITIGATING CONGESTION IN SMART CITY , 2013, DICTAP 2013.

[9]  Kenneth Tze Kin Teo,et al.  Multiple intersections traffic signal timing optimization with genetic algorithm , 2011, 2011 IEEE International Conference on Control System, Computing and Engineering.

[10]  Hui Li,et al.  Optimal Design and Real-Time Control for Energy Management in Electric Vehicles , 2011, IEEE Transactions on Vehicular Technology.

[11]  Takahiro Hara,et al.  X-Sensor: Wireless Sensor Network Testbed Integrating Multiple Networks , 2010, Wireless Sensor Network Technologies for the Information Explosion Era.

[12]  Arnaud Can,et al.  Effects of traffic signal coordination on noise and air pollutant emissions , 2012, Environ. Model. Softw..

[13]  Tadashi Dohi,et al.  A Route Discovery Method for Alleviating Traffic Congestion Based on VANETs in Urban Transportations Considering a Relation between Vehicle Density and Average Velocity , 2011, 2011 Tenth International Symposium on Autonomous Decentralized Systems.

[14]  B. Dakyo,et al.  Polynomial Control Method of DC/DC Converters for DC-Bus Voltage and Currents Management—Battery and Supercapacitors , 2012, IEEE Transactions on Power Electronics.

[15]  Chalermpol Saiprasert,et al.  Computation of driver safety rating using in-vehicle data recorder: Case study of bangkok public transportation , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[16]  Kanok Boriboonsomsin,et al.  Arterial velocity planning based on traffic signal information under light traffic conditions , 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems.

[17]  Dipti Srinivasan,et al.  Urban traffic signal control using reinforcement learning agents , 2010 .

[18]  Ozan K. Tonguz.  Notice of Violation of IEEE Publication Principles Biologically inspired solutions to fundamental transportation problems , 2011, IEEE Communications Magazine.