@article{ref1,
title="Network-wide traffic signal control based on the discovery of critical nodes and deep reinforcement learning",
journal="Journal of intelligent transportation systems: technology, planning, and operations",
year="2020",
author="Xu, Ming and Wu, Jianping and Huang, Ling and Zhou, Rui and Wang, Tian and Hu, Dongmei",
volume="24",
number="1",
pages="1-10",
abstract="To improve the traffic efficiency of city-wide road networks, we propose a traffic signal control framework that prioritizes the optimal control policies on critical nodes in road networks. In this framework, we first use a data-driven approach to discover the critical nodes. Critical nodes are identified as nodes that would cause a dramatic reduction in the traffic efficiency of the road network if they were to fail. This approach models the dynamic of road networks using a tripartite graph based on the vehicle trajectories and can accurately identify the city-wide critical nodes from a global perspective. Second, for the discovered critical nodes, we introduce a novel traffic signal control approach based on deep reinforcement learning; this approach can learn the optimal policy via constantly interacting with the road network in an iterative mode. We conduct several experiments with a transportation simulator; the results of experiments show that the proposed framework reduces the average delay and travel time compared to the baseline methods.<p /> <p>Language: en</p>",
language="en",
issn="1547-2450",
doi="10.1080/15472450.2018.1527694",
url="http://dx.doi.org/10.1080/15472450.2018.1527694"
}