A concentration of people attracted to urban areas or large events raises demand for crowd traffic control systems. Such systems need to achieve a collective control for dealing with the crowd interacting with each other, as well as a flexible control for managing the traffic depending on application scenarios such as swift passage on streets or safety-guaranteed traffic. This paper presents a Bayesian optimization (BO) approach using a multi-agent simulation for efficiently finding a control of traffic: the multi-agent simulation considers the interaction of people changing their routes for a collective control, whereas the black-box nature of BO is capable of replacing the optimization metric for different scenarios without algorithmic modification to achieve flexible traffic management. Experiments use two maps: synthetic grid-like streets and the Enoshima Coast firework festival site in Japan. In both cases, the results demonstrate that our traffic control is effective for optimizing various metrics such as an efficiency-oriented arrival rate, a safety-oriented low traffic rate, and their combination.


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    Title :

    Bayesian Optimization for Crowd Traffic Control Using Multi-Agent Simulation


    Contributors:


    Publication date :

    2019-10-01


    Size :

    1477852 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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