A spatiotemporal analysis of motorcyclist injury severity: Findings from 20 years of crash data from Pennsylvania. (March 2021)

• Record Type: Journal Article
• Authors: Li, Xiaobing; Liu, Jun; Zhang, Zihe; Parrish, Allen; Jones, Steven
• Is Part Of: Accident analysis and prevention. Volume 151(2021)
• Publication Date: 2021-03
• Subjects: Motorcyclist -- Injury severity -- Spatio-temporal -- Non-stationarity -- Clustering; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2020.105952
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: Motorcycle crash rates were much higher in metropolitan cities (e.g., Philadelphia). GTWOLR outperforms standard OLR in terms of log likelihood, Pseudo- R 2, and AIC. Most factors (e.g., helmet, speeding) exhibit significant spatiotemporal variations. Cluster spatial analysis can help target local safety issues and countermeasures. Abstract: Motorcyclists face higher risks of severe injuries in crashes compared to motor vehicle drivers who are often protected by seatbelts and airbags during collisions. A report by the National Highway Traffic Safety Administration reveals that motorcyclists have 27 times the risk of fatality in traffic crashes as much as motor vehicle drivers. Previous studies have identified a list of risk factors associated with motorcyclist injury severity and generated valuable insights for countermeasures to protect motorcyclists in crashes. These studies have shown that wearing helmets and/or motorcycle-specific reflective clothing and boots, driving alcohol/drug-free, and obeying traffic regulations are good practices for safe motorcycling. However, these practices and other risk factors are likely to interact with local geographic, socio-economic, and cultural contexts, leading to diversified correlations with motorcyclist injury severity, which remains under-explored. Such correlations may exhibit variations across space and time. The objective of this study is to revisit the correlates of motorcyclist injury severity with a focus on the spatial and temporal variations of correlations between risk factors and injury severity. This study employed an integrated spatiotemporal analytical approach to mine comprehensive statewide 20 years' motorcycle-involved traffic crashes (N = 50, 823) in Pennsylvania. Non-stationarity tests were performed to examine the significance of variations in spatially and temporally local correlations. The results show that most factors, such as helmet, engine size, vehicle age, pillion passenger, at-fault striking, and speeding, hold significant non-stationary relationships with motorcyclist injury severity. Furthermore, cluster analysis of estimations reveals the regional similarities of correlates, which may help practitioners develop regional motorcyclist safety countermeasures.