Abstract
Continuous development of urban infrastructure with a focus on sustainable transportation has led to a proliferation of vulnerable road users (VRUs), such as bicyclists and pedestrians, at intersections. Intersection safety evaluation has primarily relied on historical crash data. However, due to several limitations, including rarity, unpredictability, and irregularity of crash occurrences, quantitative and qualitative analyses of crashes may not be accurate. To transcend these limitations, intersection safety can be proactively evaluated by quantifying near-crashes using alternative measures known as surrogate safety measures (SSMs). This study focuses on developing models to predict critical near-crashes between vehicles and bicycles at intersections based on SSMs and kinematic data. Video data from ten signalized intersections in the city of San Diego were employed to train logistic regression (LR), support vector machine (SVM), and random forest (RF) models. A variation of time-to-collision called T2 and postencroachment time (PET) were used to specify monitoring periods and to identify critical near-crashes, respectively. Four scenarios were created using two thresholds of 5 and 3 s for both PET and T2. In each scenario, five monitoring period lengths were examined. The RF model was superior compared to other models in all different scenarios and across different monitoring period lengths. The results also showed a small trade-off between model performance and monitoring period length, identifying models with monitoring period lengths of 10 and 20 frames performed slightly better than those with lower or higher lengths. Sequential backward and forward feature selection methods were also applied that enhanced model performance. The best RF model had recall values of 85% or higher across all scenarios. Also, RF prediction models performed better when considering just the rear-end near-crashes with recalls of above 90%.
Highlights
With a growing interest in using eco-friendly modes of travel such as bicycling and walking, there is an increasing trend in the number of crashes involving
Considering the T2 threshold of 3 s reduced the number of events to 174 interactions, of which 56 had a postencroachment time (PET) value of less than 5 s and 46 had a PET value of less than 3 s. e decrease in the number of cases is due to the fact that a smaller threshold would naturally result in fewer observations
By increasing the length of the monitoring period, data from way before the near-crash occurrence are added to the model, which could negatively impact the model performance. erefore, a small trade-off between model performance and monitoring period length is noticeable, showing models with monitoring period lengths of 10 and 20 frames performed slightly better than those with lower or higher monitoring period lengths
Summary
With a growing interest in using eco-friendly modes of travel such as bicycling and walking, there is an increasing trend in the number of crashes involving. Is is more so the case when a specific crash type is being studied (e.g., a crash between bicyclists making left turns from an approach and vehicles going through the intersection from the opposite approach) Changes such as design improvements and demand increase could occur during such long periods, potentially impacting safety evaluation outcomes. Crash data analysis is a reactive approach in which remedial measures can be incorporated only after the occurrence of crashes, and critical locations are identified after observing multiple fatalities and injuries, and countermeasures are implemented after the fact [8] Given these shortcomings, indirect safety indicators have been studied [7, 9,10,11]. Erefore, RTTC alone could not be a good indicator of critical near-crashes To address this issue, Laureshyn et al [7] proposed T2 as the predicted time taken by the latest road user to reach the conflict points. The model results, conclusions, and potential future research are discussed
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
