Rapid Safety Assessment Tool for Non-Conventional Roadway Designs and Emerging Technologies: Innovative Artificial Intelligence Application
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2023-12-01
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Edition:Final Report December 2023
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Abstract:Traditional safety assessment methodologies are profoundly dependent on reactive crash data. The Highway Safety Manual (HSM) approach recommends 3-5 worth of crash data before and after the implementation of safety countermeasures. Waiting for such a long period of time might not be feasible to address safety issues at various roadway facilities. Furthermore, with the advent of emerging transportation technologies, rapid safety assessment tools will be required. This research provided a proof-of-concept for the development of a proactive road safety assessment framework which could be utilized for a rapid evaluation of problematic intersections, non-conventional designs, newly adopted countermeasures, as well as emerging transportation technologies. The framework is based on leveraging advanced Artificial Intelligence (AI) and machine vision to identify Surrogate Measures of Safety (SMoS) in near real-time from video cameras installed at intersections. The study established a relationship between the types of crashes and their contributing factors utilizing SMoS. Video analytics used machine vision and object detection algorithms to identify motion paths and trajectories for different road users. From estimated users’ trajectories for vehicles and pedestrians, near crashes, known as traffic conflicts, were extracted by identifying critical thresholds for the SMoS such as Time-To-Collision (TTC), Post-encroachment Time (PET), and Deceleration Rate to Avoid a Crash (DRAC). Based on the SMoS identified, and the dominating conflict patterns, safety countermeasures could be recommended. The developed methodology of this study is a first step to cost-effectively assist transportation agencies evaluating hazardous locations and safety countermeasures without the need to wait for traditional crash data.
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