Intersection and non-intersection locations are commonly used as spatial units of analysis for modeling pedestrian crashes. While both location types have been previously studied, comparing results is difficult given the different data and methods used to identify crash-risk locations. In this study, a systematic and replicable protocol was developed in GIS (Geographic Information System) to create a consistent spatial unit of analysis for use in pedestrian crash modelling. Four publicly accessible datasets were used to identify unique intersection and non-intersection locations: Roadway intersection points, roadway lanes, legal speed limits, and pedestrian crash records. Two algorithms were developed and tested using five search radii (ranging from 20 to 100 m) to assess the protocol reliability. The algorithms, which were designed to identify crash-risk locations at intersection and non-intersection areas detected 87.2% of the pedestrian crash locations (r: 20 m). Agreement rates between algorithm results and the crash data were 94.1% for intersection and 98.0% for non-intersection locations, respectively. The buffer size of 20 m generally showed the highest performance in the analyses. The present protocol offered an efficient and reliable method to create spatial analysis units for pedestrian crash modeling. It provided researchers a cost-effective method to identify unique intersection and non-intersection locations. Additional search radii should be tested in future studies to refine the capture of crash-risk locations.
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http://dx.doi.org/10.3390/ijerph16193565 | DOI Listing |
Accid Anal Prev
December 2024
Department of Civil and Environmental Engineering, Michigan State University, Lansing, MI 48910, USA. Electronic address:
Navigating intersections is a major challenge for autonomous vehicles (AVs) because of the complex interactions between different roadway user types, conflicting movements, and diverse operational and geometric features. This study investigated intersection-related AV-involved traffic conflicts by analyzing the Arogoverse-2 motion forecasting dataset to understand the driving behavior of AVs at intersections. The conflict scenarios were categorized into AV-involved and no AV conflict scenarios.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Transport Planning and Research Institute, Ministry of Transport, Beijing 100028, China.
Road traffic safety is an essential component of public safety and a globally significant issue. Pedestrians, as crucial participants in traffic activities, have always been a primary focus with regard to traffic safety. In the context of the rapid advancement of intelligent transportation systems (ITS), it is crucial to explore effective strategies for preventing pedestrian fatalities in pedestrian-vehicle crashes.
View Article and Find Full Text PDFJ Surg Res
December 2024
Division of Acute Care Surgery, Department of Surgery, Kern Medical Center, Bakersfield, California. Electronic address:
Introduction: Automobile-pedestrian (AP) crashes can cause severe injuries and are increasing in frequency. We sought to determine factors contributing to severe injuries.
Methods: Patients ≥15 y with AP injuries admitted from January 1, 2020, through December 31, 2022, comprised the study population.
Traffic Inj Prev
December 2024
School of Vehicle and Mobility, State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing, China.
Objective: Understanding pedestrians' pre-crash avoidance kinematics is crucial for improving the identification of potential collision areas in interactions with highly automated vehicles (HAVs). Age significantly influences pedestrian avoidance velocity and the subsequent crash risks. However, current active safety systems in HAVs often overlook the influence of pedestrians' avoidance velocity and age on imminent accidents.
View Article and Find Full Text PDFAccid Anal Prev
December 2024
Civil Engineering Division, Department of Engineering, University of Cambridge, Cambridge CB3 0FA, United Kingdom. Electronic address:
Shared spaces prioritise the role of micromobility in urban environments by separating vulnerable road users from motorised vehicles, aiming to enhance both actual and perceived safety. However, the presence of various transport modes, such as pedestrians, cyclists and e-scooters, with differing navigation behaviours, increases the heterogeneity of these spaces and impacts the perception of safety. Despite the increasing use of e-scooters, the safety perceptions of e-scooter riders remain largely underexplored in the literature.
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