Deep Reinforcement Learning for Adaptive Traffic Signal Control

Autor:	Tan, Kai Liang, Poddar, Subhadipto, Sharma, Anuj, Sarkar, Soumik
Rok vydání:	2019
Předmět:	Electrical Engineering and Systems Science - Systems and Control Computer Science - Machine Learning Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	Many existing traffic signal controllers are either simple adaptive controllers based on sensors placed around traffic intersections, or optimized by traffic engineers on a fixed schedule. Optimizing traffic controllers is time consuming and usually require experienced traffic engineers. Recent research has demonstrated the potential of using deep reinforcement learning (DRL) in this context. However, most of the studies do not consider realistic settings that could seamlessly transition into deployment. In this paper, we propose a DRL-based adaptive traffic signal control framework that explicitly considers realistic traffic scenarios, sensors, and physical constraints. In this framework, we also propose a novel reward function that shows significantly improved traffic performance compared to the typical baseline pre-timed and fully-actuated traffic signals controllers. The framework is implemented and validated on a simulation platform emulating real-life traffic scenarios and sensor data streams. Comment: ASME 2019 Dynamic Systems and Control Conference (DSCC), October 9-11, Park City, Utah, USA
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1911.06294 Zobrazit plný text záznamu View this record from Arxiv