Evaluating the reliability of automatically generated pedestrian and bicycle crash surrogates

• URL: http://arxiv.org/abs/2307.13178v1
• Date: Mon, 24 Jul 2023 23:57:29 GMT
• Title: Evaluating the reliability of automatically generated pedestrian and bicycle crash surrogates
• Authors: Agnimitra Sengupta, S. Ilgin Guler, Vikash V. Gayah, Shannon Warchol
• Abstract summary: Vulnerable road users (VRUs) are at a higher risk of being involved in crashes with motor vehicles. Signalized intersections are a major safety concern for VRUs due to their complex and dynamic nature. This research builds on a study to assess the reliability of automatically generated surrogates in predicting confirmed conflicts.
• Score: 0.491574468325115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vulnerable road users (VRUs), such as pedestrians and bicyclists, are at a higher risk of being involved in crashes with motor vehicles, and crashes involving VRUs also are more likely to result in severe injuries or fatalities. Signalized intersections are a major safety concern for VRUs due to their complex and dynamic nature, highlighting the need to understand how these road users interact with motor vehicles and deploy evidence-based countermeasures to improve safety performance. Crashes involving VRUs are relatively infrequent, making it difficult to understand the underlying contributing factors. An alternative is to identify and use conflicts between VRUs and motorized vehicles as a surrogate for safety performance. Automatically detecting these conflicts using a video-based systems is a crucial step in developing smart infrastructure to enhance VRU safety. The Pennsylvania Department of Transportation conducted a study using video-based event monitoring system to assess VRU and motor vehicle interactions at fifteen signalized intersections across Pennsylvania to improve VRU safety performance. This research builds on that study to assess the reliability of automatically generated surrogates in predicting confirmed conflicts using advanced data-driven models. The surrogate data used for analysis include automatically collectable variables such as vehicular and VRU speeds, movements, post-encroachment time, in addition to manually collected variables like signal states, lighting, and weather conditions. The findings highlight the varying importance of specific surrogates in predicting true conflicts, some being more informative than others. The findings can assist transportation agencies to collect the right types of data to help prioritize infrastructure investments, such as bike lanes and crosswalks, and evaluate their effectiveness.

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