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Koh, SS, Zhou, B, Yang, P, Yang, Z, Fang, H and Feng, J (2019) Reinforcement Learning for Vehicle Route Optimization in SUMO. In: 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS) . (The 16th IEEE Conference on Smart City, 28 - 30 June 2018, Exeter, UK).
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Abstract
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.
| Item Type: | Conference or Workshop Item (Paper) |
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| Additional Information: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) T Technology > TL Motor vehicles. Aeronautics. Astronautics |
| Divisions: | Computer Science & Mathematics |
| Publisher: | IEEE |
| Date Deposited: | 27 Sep 2018 10:04 |
| Last Modified: | 13 Apr 2022 15:16 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/9338 |
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