Bicyclist injury severity in traffic crashes: A spatial approach for geo-referenced crash data to uncover non-stationary correlates. (June 2020)

• Record Type: Journal Article
• Authors: Liu, Jun; Khattak, Asad J.; Li, Xiaobing; Nie, Qifan; Ling, Ziwen
• Is Part Of: Journal of safety research. Volume 73(2020)
• Page Start: 25
• Page End: 35
• Publication Date: 2020-06
• Subjects: Bicycle--motor-vehicle crash -- Bicyclist injury severity -- Non-stationarity -- Geographically weighted ordinal logistic regression; Industrial safety -- Periodicals; Accidents -- Prevention -- Periodicals; Safety -- Periodicals; Accidents, Occupational -- Periodicals; Sécurité du travail -- Périodiques; Accidents -- Prévention -- Périodiques; Accidents -- Prevention; Industrial safety; Periodicals; 363.1
• DOI: 10.1016/j.jsr.2020.02.006
• Languages: English
• ISSNs: 0022-4375

Abstract

Highlights: The study uncovers non-stationary correlates of bicyclist injury severity in traffic crashes. The modeling approach accounts for the spatial heterogeneity in traffic crashes. The study explored geo-referenced bicycle-motor vehicle crashes in North Carolina. Results show correlates of bicyclist injury severity vary substantially across North Carolina. Modeling results can be used to localize bicycling safety countermeasures. Abstract: Introduction: Bicyclists are among vulnerable road users with their safety a key concern. This study generates new knowledge about their safety by applying a spatial modeling approach to uncover non-stationary correlates of bicyclist injury severity in traffic crashes. Method: The approach is Geographically Weighted Ordinal Logistic Regression (GWOLR), extended from the regular Ordered Logistic Regression (OLR) by incorporating the spatial perspective of traffic crashes. The GWOLR modeling approach allows the relationships between injury severity and its contributing factors to vary across the spatial domain, to account for the spatial heterogeneity. This approach makes use of geo-referenced data. This study explored more than 7, 000 geo-referenced bicycle--motor-vehicle crashes in North Carolina. Results: This study performed a series of non-stationarity tests to identify local relationships that vary substantially across the spatial domain. These local relationships are related to the bicyclist (bicyclist age, bicyclist behavior, bicyclist intoxication, bicycle direction, bicycle position), motorist (driver age, driver intoxication, driver behavior, vehicle speed, vehicle type) and traffic (traffic volume). Conclusions: Results from the regular OLR are in general consistent with previous findings. For example, an increased bicyclist injury severity is associated with older bicyclists, bicyclist being intoxicated, and higher motor-vehicle speeds. Results from the GWOLR show local (rather than global) relationships between contributing factors and bicyclist injury severity. Practical Applications: Researchers and practitioners may use GWOLR to prioritize cycling safety countermeasures for specific regions. For example, GWOLR modeling estimates in the study highlighted the west part (from Charlotte to Asheville) of North Carolina for increased bicyclist injury severity due to the intoxication of road users including both bicyclists and drivers. Therefore, if a countermeasure is concerned with the road user intoxication, there may be a priority for the region from Charlotte to Asheville (relative to other areas in North Carolina).