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Open Access Publications from the University of California

Analysis of Intelligent Vehicle Technologies to Improve Vulnerable Road Users Safety at Signalized Intersections

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This project aims to know how the Intelligent Vehicle Technologies (IVT) can improve Vulnerable Road Users’ (VRU) safety in different environments and conditions (e.g., sight distance and traffic flow) at signalized intersections. For the statistical analysis on historical aggregate crash data, the project studied risk factors on crash injury severity for VRU-related crashes at signalized intersections in California cities. The researchers summarize seven critical crash types for the micro-level traffic safety simulation. For the traffic safety simulation part, it is found that Intersection Safety (INS) is empowered to be the most efficient technology to significantly reduce average collision counts for passenger cars under all seven collision types of interest. Blind Spot Detection (BSD) has the most minimal effects on those types. The safety improvement of VRU Beacon Systems (VBS) and Bicycle/Pedestrian to Vehicle Communication (BPTV) are between INS and BSD. Results show that under a certain threshold of sight distance, IVT can significantly reduce the collision probability and IVT can still improve safety under good sight condition if collisions happen in front of vehicles. In the end, the project conducted sensitive analyses of sight distance and traffic volume. For some collision types, INS and BPTV can only reduce ~50% of collision at extremely high traffic volume conditions.

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