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Interactive traffic simulation model with learned local parameters

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Abstract

In this paper, we present a parameter learning method to reflect the rapidly changing behaviors in the traffic flow simulation process, in which we insert virtual vehicles into the real trajectory data. We come up with a real-virtual interaction model and then we use genetic algorithm to learn some parameters in the model with the purpose to get some specific driving characteristics. Then we propose a real-virtual interaction system to vividly simulate the various interaction behaviors between the real vehicles and the virtual ones. Our results are compared to the existing methods to prove the effectiveness of our presented method.

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Acknowledgments

The authors wish to acknowledge the support of NSFC grant 61300084, 61370141, 61300015, 91546123, 11372067, and 61425002, National High-tech R&D Program of China (Grant No. 2015AA7046207), the Open Project Program of the State Key Lab of CAD&CG (Grant No. A1511), Zhejiang University, and the Fundamental Research Funds for the Central Universities (Grant No. DUT15QY41).

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Authors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Xin Yang, Shuai Li, Yong Zhang, Wanchao Su, Mingyue Zhang, Guozhen Tan & Xiaopeng Wei

  2. Dalian University, Dalian, China

    Qiang Zhang & Dongsheng Zhou

Authors
  1. Xin Yang
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  2. Shuai Li
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  3. Yong Zhang
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  4. Wanchao Su
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  5. Mingyue Zhang
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  6. Guozhen Tan
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  7. Qiang Zhang
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  8. Dongsheng Zhou
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  9. Xiaopeng Wei
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Corresponding author

Correspondence to Xin Yang.

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Yang, X., Li, S., Zhang, Y. et al. Interactive traffic simulation model with learned local parameters. Multimed Tools Appl 76, 9503–9516 (2017). https://doi.org/10.1007/s11042-016-3560-6

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  • DOI: https://doi.org/10.1007/s11042-016-3560-6

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