Objective: Intersection movement assist (IMA) has been recognized as one of the prominent countermeasures to reduce angle crashes at intersections, which constitute 22% of total crashes in the United States. Utilizing vehicle-based sensors, vehicle-to-vehicle (V2V), and vehicle-to-infrastructure (V2I) communications, IMA offers extended vision to provide early warning for an imminent crash. However, most of IMA-related research implements their methods and strategies only in simulations, test tracks, or driving simulator studies that have quite a few assumptions and limitations and hence the effectiveness evaluations reported may not be transferable or comparable.
Methods: This study seeks to develop a generalized evaluation scheme that can be used not only to assess the effectiveness of IMA on improving traffic safety at intersections but to facilitate comparisons across similar studies. The proposed evaluation scheme utilizes the concepts of traffic conflict in terms of time-to-collision (TTC) as a crash surrogate. This approach avoids the issue of having insufficient crash frequency data for system evaluation. To measure the effectiveness of IMA on reducing traffic conflicts, a relative risk is calculated for comparing the risk of with/without using the IMA. As a proof-of-concept study, this study applied the proposed evaluation scheme and reported the effectiveness of IMA on improving traffic safety in a field operation test (FOT). Seven test scenarios were conducted at 4 intersections, and a total of 40 participants were recruited to use the IMA for 6 months.
Results: It was estimated that IMA users have 26% fewer conflicts with TTC less than 5 s and have 15% fewer conflicts with TTC less than 4 s. However, the results vary across different sites and different definitions of conflicts in terms of TTC.
Conclusions: Overall, IMA is promising to effectively reduce angle crashes related to sight obstruction and has potential to reduce not only crash frequency but crash severity. 相似文献
Problem: Potential conflicts between pedestrians and vehicles represent a challenge to pedestrian safety. Near-crash is used as a surrogate metric for pedestrian safety evaluations when historical vehicle–pedestrian crash data are not available. One challenge of using near-crash data for pedestrian safety evaluation is the identification of near-crash events. Method: This paper introduces a novel method for pedestrian-vehicle near-crash identification that uses a roadside LiDAR sensor. The trajectory of each road user can be extracted from roadside LiDAR data via several data processing algorithms: background filtering, lane identification, object clustering, object classification, and object tracking. Three indicators, namely, the post encroachment time (PET), the proportion of the stopping distance (PSD), and the crash potential index (CPI) are applied for conflict risk classification. Results: The performance of the developed method was evaluated with field-collected data at four sites in Reno, Nevada, United States. The results of case studies demonstrate that pedestrian-vehicle near-crash events could be identified successfully via the proposed method. Practical applications: The proposed method is especially suitable for pedestrian-vehicle near-crash identification at individual sites. The extracted near-crash events can serve as supplementary material to naturalistic driving study (NDS) data for safety evaluation. 相似文献
