@article{ref1,
title="A deep reinforcement learning-based intelligent intervention framework for real-time proactive road safety management",
journal="Accident analysis and prevention",
year="2021",
author="Roy, Ananya and Hossain, Moinul and Muromachi, Yasunori",
volume="165",
number="",
pages="e106512-e106512",
abstract="We propose a variable speed limit (VSL) system for improving the safety of urban expressways in real time. The system has two main functions: monitoring traffic data and then using the data to assess crash risk through a real-time crash prediction model (RTCPM). When the risk is high, the system triggers VSL control to restore traffic conditions to normal. The study addresses several weaknesses in existing VSL-based real-time safety interventions. Existing models are not widely applicable due to varying detector spacing among different freeways, and even within a study area. Therefore, with the existing detector spacing as an input, a cell transmission model (CTM) is used to simulate traffic states for the desired cell size. A dynamic Bayesian network (DBN) is used for modeling in the RTCPM. The proposed CTM model is then modified to allow VSL control. Whereas existing studies selected various VSL strategies from a predefined list, we employ a deep Q-network, which is a reinforcement learning-based machine learning algorithm, for the VSL control. Two busy segments of the Tokyo Metropolitan Expressway were used as the study area. After several iterations, our proposed real-time system reduced the crash risk by 19%.<p /> <p>Language: en</p>",
language="en",
issn="0001-4575",
doi="10.1016/j.aap.2021.106512",
url="http://dx.doi.org/10.1016/j.aap.2021.106512"
}