Traffic signal retiming and coordination play a significant role in traffic management, especially when the traffic incidents disrupt the network. Many researchers have developed artificially intelligent (AI) traffic signal controllers based on goal-oriented machine learning frameworks to try and optimize network performance. However, previous efforts have lacked the means to evaluate these networks under traffic incident conditions. To make these AI traffic signal controllers more robust, current research needs to consider AI traffic controller performance under traffic incidents and accompanying emergency response. Obtaining field incident data and converting into inputs for simulation models to evaluate these machine learning models has been a huge hurdle because it is expensive, time-consuming, and sometimes even unfeasible. This paper provides an integrated traffic incident and response simulation tool for a grid network made in Simulation of Urban MObility (SUMO) to overcome this gap. The tool includes random traffic incident generation (location and duration), incident detection, random emergency vehicle generation, and emergency vehicle dispatching. With this tool, users can simulate a road network with traffic incidents and emergency vehicle response to produce substantial amounts of data for training robust reinforcement learning models. In addition, this tool will save future researchers and practitioners both time and effort in testing the impact of their proposed AI traffic control models and allow for a more complete evaluation of performance.


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    Titel :

    A Python Extension in Sumo for Simulating Traffic Incidents and Emergency Service Vehicles


    Weitere Titelangaben:

    Lecture Notes in Civil Engineering


    Beteiligte:
    Gupta, Rishi (Herausgeber:in) / Sun, Min (Herausgeber:in) / Brzev, Svetlana (Herausgeber:in) / Alam, M. Shahria (Herausgeber:in) / Ng, Kelvin Tsun Wai (Herausgeber:in) / Li, Jianbing (Herausgeber:in) / El Damatty, Ashraf (Herausgeber:in) / Lim, Clark (Herausgeber:in) / Li, Tianxin (Autor:in) / Zhao, Wei (Autor:in)

    Kongress:

    Canadian Society of Civil Engineering Annual Conference ; 2022 ; Whistler, BC, BC, Canada May 25, 2022 - May 28, 2022



    Erscheinungsdatum :

    2024-02-06


    Format / Umfang :

    13 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch







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