@article{ref1,
title="A spatiotemporal analysis of motorcyclist injury severity: findings from 20 years of crash data from Pennsylvania",
journal="Accident analysis and prevention",
year="2020",
author="Li, Xiaobing and Liu, Jun and Zhang, Zihe and Parrish, Allen and Jones, Steven",
volume="151",
number="",
pages="e105952-e105952",
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.<p /> <p>Language: en</p>",
language="en",
issn="0001-4575",
doi="10.1016/j.aap.2020.105952",
url="http://dx.doi.org/10.1016/j.aap.2020.105952"
}