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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2009  >  9(6): 114-120
WEI Yun, FAN Bing-quan, HAN Yin, GAN Hong-cheng. Impact of traffic guidance information on vehicle behavior in network[J]. Journal of Traffic and Transportation Engineering, 2009, 9(6): 114-120. doi: 10.19818/j.cnki.1671-1637.2009.06.022
Citation: WEI Yun, FAN Bing-quan, HAN Yin, GAN Hong-cheng. Impact of traffic guidance information on vehicle behavior in network[J]. Journal of Traffic and Transportation Engineering, 2009, 9(6): 114-120. doi: 10.19818/j.cnki.1671-1637.2009.06.022
shu

Impact of traffic guidance information on vehicle behavior in network

doi: 10.19818/j.cnki.1671-1637.2009.06.022
  • 1.

    School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

  • 2.

    School of Business, University of Shanghai for Science and Technology, Shanghai 200093, China

More Information
  • Author Bio:

    WEI Yun(1976-), female, lecturer, doctoral student, +86-21-55275835, yun_wei@sohu.com

    FAN Bing-quan(1942-), male, professor, +86-21-55275273, bqfan@usst.edu.cn

  • Received Date: 2009-08-01
  • Publish Date: 2009-12-25
    Abstract
  • To study the effect of traffic guidance model, cellular automata(CA)model was applied to simulate vehicles' behaviors in traffic network, and the impacts of different guidance informations on traffic flow were studied in different traffic volumes and different accepting guidance ratios.A traffic guidance model based on agent technology was built.In which Q-learning algorithm was used to optimize traffic guidance information, propositional information could be provided according to real time network traffic, traffic flow distribution was adjusted.Simulation result shows that the factors that impact guidance effect in network are accepting guidance ratio and guidance information, traffic guidance model based on agent can effectively balance network traffic flow, which is better to heavy traffic flow.In light traffic flow, the model has a little advantage compared with normal model, but in heavy traffic flow, propositional guidance information can decrease 12% average travel time compared with descriptive guidance information.

     

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