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Deep Learning and Case-based Reasoning for Predictive and Adaptive Traffic Emergency Management by Ali Louati; Hassen Louati; Zhaojian Li is a Computer Science article available to read on EtoBox.

What is Deep Learning and Case-based Reasoning for Predictive and Adaptive Traffic Emergency Management about?

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 casebased 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.

Who reads Deep Learning and Case-based Reasoning for Predictive and Adaptive Traffic Emergency Management?

Author

Ali Louati; Hassen Louati; Zhaojian Li

Publisher

Springer Science and Business Media LLC

Published

2020

Language

EN

Field

Computer Science (Physical Sciences)