Interactive traffic simulation model with learned local parameters

Xin Yang, Shuai Li, Yong Zhang, Wanchao Su, Mingyue Zhang, Guozhen Tan, Qiang Zhang, Dongsheng Zhou, Xiaopeng Wei

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)9503-9516
Number of pages14
JournalMultimedia Tools and Applications
Issue number7
Publication statusPublished - Apr 2017
Externally publishedYes


  • Genetic algorithm
  • Real-virtual interaction
  • Traffic simulation

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