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An urban traffic simulation model for traffic congestion predicting and avoiding

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Abstract

Urban traffic congestion is a common problem that affects many cities around the world. In this paper, an actual urban traffic simulation model (AUTM) for traffic congestion predicting and avoiding is proposed, which includes three key components: the map and transfer (MT) conversion method, the optimized spatial evolution rules, and a congestion-avoidance routing algorithm. Three key techniques are combined in our proposed model: (1) The MT conversion method is proposed to get actual urban cellular spaces, which apply the optimized spatial evolution rules to simulate the vehicular dynamics better. (2) AUTM is proposed for simulating traffic congestion and predicting the effect of adding overpasses and roadblocks. (3) The congestion-avoidance routing algorithm is proposed for vehicles to dynamically update their routes toward their destinations, which can achieve traffic optimization in urban simulations. This paper presents the results of applying this novel model to a large-scale real-world case in different urban traffic congestion situations. Extensive experimental simulations in various actual cities have been carried out. Our results in the extreme case are encouraging: The prediction accuracy of traffic congestions is almost 89%, and the variance of prediction road density is less than 0.15.

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Acknowledgements

This work is partially supported by National Natural Science Foundation of China (61572369, 61471274); National Natural Science Foundation of Hubei Province (2015CFB423); Wuhan Major Science and Technology Program (2015010101010023).

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Authors and Affiliations

  1. School of Computer, Wuhan University, Wuhan City, 430072, Hubei Province, China

    Wenbin Hu, Huan Wang, Zhenyu Qiu, Liping Yan, Cong Nie & Bo Du

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  1. Wenbin Hu
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  2. Huan Wang
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  3. Zhenyu Qiu
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  4. Liping Yan
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  5. Cong Nie
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  6. Bo Du
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Correspondence to Wenbin Hu.

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Hu, W., Wang, H., Qiu, Z. et al. An urban traffic simulation model for traffic congestion predicting and avoiding. Neural Comput & Applic 30, 1769–1781 (2018). https://doi.org/10.1007/s00521-016-2785-7

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  • DOI: https://doi.org/10.1007/s00521-016-2785-7

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