Abstract
Vehicle-pedestrian conflicts have been the major concern for traffic safety. Surrogate safety measures are widely applied for pedestrian safety evaluation. However, how to quickly identify the vehicle-pedestrian surrogate safety measures at the individual site is challenging due to the difficulty of obtaining the high-resolution trajectories of road users. This paper presented an effective method to generate the high-resolution traffic trajectories from the roadside deployed Light Detection and Ranging (LiDAR) sensor. The vehicle-pedestrian conflicts can then be identified from the trajectories simply using the speed-distance profile (SDP) of the vehicles. The SDP can be used to develop a rule-based method for vehicle-pedestrian identification. The events can be divided into different risk levels based on the spatial distribution of the SDP. The case study shows that the rule-based method can detect vehicle-pedestrian near-crash events effectively. The other indicators, such as widely used time-to-collision (TTC) or deceleration rate to avoid a crash (DRAC), can be also obtained from the SDP. The engineers can also adjust the thresholds in the rule-based method to meet the specific requirements at different sites. The proposed method can be extended to identify vehicle-vehicle conflicts or vehicle-bicycle conflicts in future studies.
Highlights
Pedestrians are vulnerable groups on the roads compared to motor vehicles
According to the traffic safety facts released by National Highway Traffic Safety Administration (NHTSA), a total of 79,000 pedestrians were involved in vehicle-pedestrian crashes, including 5,977 fatalities in the United States in 2017 [2]
This paper developed a systematic method for vehiclepedestrian conflicts identification using the HRMTD extracted from the roadside Light Detection and Ranging (LiDAR) data
Summary
Pedestrians are vulnerable groups on the roads compared to motor vehicles. The vehicle-pedestrian conflict has been a major concern of public health worldwide [1]. Ismail et al [16] developed an automatic procedure that can extract the vehicle-pedestrian conflicts using video data The indicators such as TTC, deceleration-to-safety time (DST), and post-encroachment-time (PET) can be calculated using the proposed method. Simulationbased methods can be a good approach to analyze vehiclepedestrian conflicts under a specific environment, and to validate the performance of different indicators, but may not be effective to assess pedestrian safety for massive real sites. This paper developed a systematical procedure to extract the high-resolution traffic data from the roadside LiDAR sensors Those trajectories were used as the input to evaluate the feasibility of different indicators. The extracted surrogate safety measures can be either used for pedestrian safety evaluation or be possibly used to develop the near-crash warning system for connected-vehicles since the real-time identification can be achieved in the future. The last section summarizes the findings and provides the research directions for future studies
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