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

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...

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Bibliographic Details
Published in:The Journal of supercomputing 2021-05, Vol.77 (5), p.4389-4418
Main Authors: Louati, Ali, Louati, Hassen, Li, Zhaojian
Format: Article
Language:English
Subjects:
Adaptive control
Algorithms
Artificial neural networks
Compilers
Computer Science
Convolution
Deep learning
Emergency management
Interpreters
Multiagent systems
Preempting
Processor Architectures
Programming Languages
Reasoning
Traffic control
Traffic flow
Traffic management
Traffic signals
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Summary: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.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-020-03435-3