Urban traffic control becomes a major topic for urban development lately as the growing number of vehicles in the transportation network. Recent advances in reinforcement learning methodologies have shown highly potential results in solving complex traffic control problem with multi-dimensional states and actions. It offers an opportunity to build a sustainable and resilient urban transport network for a variety of objects, such as minimizing the fuel consumption or improving the safety of roadway. Inspired by this promising idea, this paper presents an experience how to apply reinforcement learning method to optimize the route of a single vehicle in a network. This experience uses an open-source simulator SUMO to simulate the traffic. It shows promising result in finding the best route and avoiding the congestion path.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Reinforcement Learning for Vehicle Route Optimization in SUMO


    Contributors:
    Koh, Song Sang (author) / Zhou, Bo (author) / Yang, Po (author) / Yang, Zaili (author) / Fang, Hui (author) / Feng, Jianxin (author)


    Publication date :

    2018-06-01


    Size :

    429403 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Customized Bus Route Optimization Based on Reinforcement Learning

    Wang, Ange / Peng, Liqun / Qin, Zhengtao et al. | ASCE | 2020


    Multi-Objective Deep Reinforcement Learning for Crowd Route Guidance Optimization

    Nishida, Ryo / Tanigaki, Yuki / Onishi, Masaki et al. | Transportation Research Record | 2023


    Optimization of Welding Route by Automatic Machine Using Reinforcement Learning Method

    Okumoto, Y. | British Library Conference Proceedings | 2008


    Roadtest - Subaru Sumo

    Sowman,C. / Subaru Motor,JP | Automotive engineering | 1989