Data on Risk Management Reported by Ananya Roy and Colleagues (A deep reinforcement learning-based intelligent intervention framework for real-time proactive road safety management): Risk Management

2021 DEC 23 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- New research on Risk Management is the subject of a report. According to news originating from Tokyo, Japan, by NewsRx correspondents, research stated, “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).”

Financial support for this research came from Japan Society for the Promotion of Science.

Our news journalists obtained a quote from the research, “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.”

According to the news editors, the research concluded: “After several iterations, our proposed real-time system reduced the crash risk by 19%.”

This research has been peer-reviewed.

For more information on this research see: A deep reinforcement learning-based intelligent intervention framework for real-time proactive road safety management. Accident Analysis & Prevention, 2021;165:106512. Accident Analysis & Prevention can be contacted at: Pergamon-elsevier Science Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England. (Elsevier - www.elsevier.com; Accident Analysis & Prevention - http://www.journals.elsevier.com/accident-analysis-and-prevention/)

The news correspondents report that additional information may be obtained from Ananya Roy, ALMEC Corporation, Head Office Transportation Planning Division (Overseas Department), Kensei Shinjuku Building, 5-5-3 Shinjuku, Shinjuku-ku, Tokyo 160-0022, Japan. Additional authors for this research include Moinul Hossain and Yasunori Muromachi.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.aap.2021.106512. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

The publisher’s contact information for the journal Accident Analysis & Prevention is: Pergamon-elsevier Science Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, England.

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