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Cognition-Driven Traffic Simulation for Unstructured Road Networks

School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China
School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China
School of Software, Zhengzhou University of Light Industry, Zhengzhou 450002, China
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Dynamic changes of traffic features in unstructured road networks challenge the scene-cognitive abilities of drivers, which brings various heterogeneous traffic behaviors. Modeling traffic with these heterogeneous behaviors would have significant impact on realistic traffic simulation. Most existing traffic methods generate traffic behaviors by adjusting parameters and cannot describe those heterogeneous traffic flows in detail. In this paper, a cognition-driven trafficsimulation method inspired by the theory of cognitive psychology is introduced. We first present a visual-filtering model and a perceptual-information fusion model to describe drivers’ heterogeneous cognitive processes. Then, logistic regression is used to model drivers’ heuristic decision-making processes based on the above cognitive results. Lastly, we apply the high-level cognitive decision-making results to low-level traffic simulation. The experimental results show that our method can provide realistic simulations for the traffic with those heterogeneous behaviors in unstructured road networks and has nearly the same efficiency as that of existing methods.

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Journal of Computer Science and Technology
Pages 875-888
Cite this article:
Wang H, He X-Y, Chen L-Y, et al. Cognition-Driven Traffic Simulation for Unstructured Road Networks. Journal of Computer Science and Technology, 2020, 35(4): 875-888.






Web of Science






Received: 30 March 2019
Revised: 09 March 2020
Published: 27 July 2020
©Institute of Computing Technology, Chinese Academy of Sciences 2020