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SALA: A Self-Adaptive Learning Algorithm—Towards Efficient Dynamic Route Guidance in Urban Traffic Networks

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Abstract

In order to alleviate traffic congestion for vehicles in urban networks, most of current researches mainly focused on signal optimization models and traffic assignment models, or tried to recognize the interaction between signal control and traffic assignment. However, these methods may not be able to provide fast and accurate route guidance due to the lack of individual traffic demands, real-time traffic data and dynamic cooperation between vehicles. To solve these problems, this paper proposes a dynamic and real-time route selection model in urban traffic networks (DR2SM), which can supply a more accurate and personalized strategy for vehicles in urban traffic networks. Combining the preference for alternative routes with real-time traffic conditions, each vehicle in urban traffic networks updates its route selection before going through each intersection. Based on its historical experiences and estimation about route choices of the other vehicles, each vehicle uses a self-adaptive learning algorithm to play congestion game with each other to reach Nash equilibrium. In the route selection process, each vehicle selects the user-optimal route, which can maximize the utility of each driving vehicle. The results of the experiments on both synthetic and real-world road networks show that compared with non-cooperative route selection algorithms and three state-of-the-art equilibrium algorithms, DR2SM can effectively reduce the average traveling time in the dynamic and uncertain urban traffic networks.

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Acknowledgements

This work is partially supported by National Natural Science Foundation of China (61572369, 61711530238); National Natural Science Foundation of Hubei Province (2015CFB423); Wuhan Major Science and Technology Program (2015010101010023); Science and Technology Project of Jiangxi Provincial Education Department (GJJ160494, GJJ160500) and Jiangxi Province Youth Science Foundation (20151BAB217017). The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.

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Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, Hubei Province, China

    Liping Yan & Wenbin Hu

  2. School of Software, East China Jiaotong University, Nanchang, Jiangxi Province, China

    Liping Yan

  3. Civil and Environmental Engineering Department, Imperial College London, London, UK

    Simon Hu

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  1. Liping Yan
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  2. Wenbin Hu
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  3. Simon Hu
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Correspondence to Wenbin Hu.

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Yan, L., Hu, W. & Hu, S. SALA: A Self-Adaptive Learning Algorithm—Towards Efficient Dynamic Route Guidance in Urban Traffic Networks. Neural Process Lett 50, 77–101 (2019). https://doi.org/10.1007/s11063-018-9870-0

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