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Deep learning and case-based reasoning for predictive and adaptive traffic emergency management

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Abstract

An efficient traffic signal control system (TSCS) should not only be reactive to the current traffic but also be predictive by anticipating future traffic disturbances. In this study, we investigate the potential of using convolution neural network (CNN) in detecting emergency cases and forecasting events that can interrupt the traffic flow. Case-based reasoning (CBR) is then exploited to react to detected and forecasted events. We further develop an adapted Reinforcement Leaning (RL) algorithm in building and enhancing the case bases. The proposed system inherits the advantages of CNN, CBR, and RL, which allow detection, prediction, control, evaluation, and learning in a unified framework. To assess the proposed TSCS, we compare our approach with a set of state-of-art algorithms (e.g., multi-agent preemptive case-based reasoning algorithm and multi-agent preemptive longest queue first—maximal weight matching). The proposed TSCS outperforms the benchmarking algorithms through experiments in various traffic scenarios.

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Acknowledgements

The authors want to thank the Deanship of Scientific Research in Prince Sattam Bin Abdulaziz University for supporting and funding the corresponding author research related to traffic control and emergency management since 2018 until 2020. Moreover, the authors want to thank the nine reviewers for; 1) the time spent on evaluating the manuscript, 2) appreciating the novelty of our suggestions, and 3) finding the contributions interesting and innovative.

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Authors and Affiliations

  1. Information Systems Department, Prince Sattam Bin Abdulaziz University, Al-Kharj, Kingdom of Saudi Arabia

    Ali Louati

  2. ISG, SMART Lab, University of Tunis, 41, Avenue de la Liberté, Bouchoucha, Le Bardo, 2000, Tunis, Tunisia

    Ali Louati & Hassen Louati

  3. Polytechnic University of Bucharest, Bucharest, Romania

    Hassen Louati

  4. Michigan State University, East Lansing, USA

    Zhaojian Li

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  1. Ali Louati
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Correspondence to Ali Louati.

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Louati, A., Louati, H. & Li, Z. Deep learning and case-based reasoning for predictive and adaptive traffic emergency management. J Supercomput 77, 4389–4418 (2021). https://doi.org/10.1007/s11227-020-03435-3

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