PMID- 31169068
OWN - NLM
STAT- MEDLINE
DCOM- 20200117
LR  - 20200117
IS  - 1745-7319 (Electronic)
IS  - 1745-7300 (Linking)
VI  - 26
IP  - 3
DP  - 2019 Sep
TI  - Exploring the factors contribute to the injury severities of vulnerable roadway 
      user involved crashes.
PG  - 302-314
LID - 10.1080/17457300.2019.1595665 [doi]
AB  - The vehicle to pedestrian (V2P) applications will enable safety, mobility, and 
      environmental advancements for the vulnerable roadway user (VRU) that current 
      technologies are unable to provide. The present research aims to explore the use 
      of random parameters in logit models to examine factors that significantly 
      influence injury severity of VRU involved crashes. Two types of logit models, the 
      mixed generalized ordered logit (MGOL) models and mixed logit models are proposed 
      to provide insights on reducing injury severities of pedestrian and bicyclist 
      involved crashes and benefit amending current V2P applications to address the 
      special safety needs and challenges of these VRUs. Based on 9180 pedestrian 
      involved crashes and 1402 bicyclist involved crashes from the Fatality Analysis 
      Reporting System (FARS), the measure of injury severities - time-to-death is 
      considered as the independent variables to capture a more comprehensive picture 
      of events after a crash occurs. By comparing to the ordered logit models and the 
      multinomial logit models, the effectiveness and appropriateness of the proposed 
      models are verified through two perspectives - goodness of fit and predictive 
      power. The modelling results show that the injury severity of VRU involved 
      crashes is significantly associated with involved non-motorist characteristics 
      (age and police reported alcohol involvement), involved motorist characteristics 
      (drunk drivers, previous recorded crashes, number of occupants), involved vehicle 
      characteristics (vehicle body type, vehicle model year, travel speed), roadway 
      characteristics (interstate, junction, roadway profile), and environmental 
      characteristics (light and weather condition). Among these significant factors, 
      the number of occupants, vehicle body type, interstate, and junction result in 
      random parameters, which capture and reflect the unobserved heterogeneity across 
      sampled observations. The analyses of under-researched aspects of VRU involved 
      crashes, that is time-to-death, help us develop a deeper understanding of the 
      consequences of injury and ultimately health and social costs. The findings 
      indicate that the proposed MGOL models and mixed logit models can account for the 
      heterogeneity issues in crash data due to the unobserved factors. In addition, 
      the injury severity models that incorporate the random parameter features can 
      reveal new insights and have superior goodness of fit.
FAU - Dong, Chunjiao
AU  - Dong C
AD  - a Key Laboratory of Transport Industry of Big Data Application Technologies for 
      Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University , 
      China.
AD  - b Center for Transportation Research, The University of Tennessee , Knoxville , 
      TN , USA.
FAU - Khattak, Asad J
AU  - Khattak AJ
AUID- ORCID: 0000-0002-0790-7794
AD  - c Department of Civil & Environmental Engineering, The University of Tennessee , 
      Knoxville , TN , USA.
FAU - Shao, Chunfu
AU  - Shao C
AD  - a Key Laboratory of Transport Industry of Big Data Application Technologies for 
      Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University , 
      China.
FAU - Xie, Kun
AU  - Xie K
AD  - d National Demonstration Center for Experimental Traffic and Transportation 
      Education, School of Traffic and Transportation, Beijing Jiaotong University , 
      Beijing , China.
LA  - eng
PT  - Journal Article
DEP - 20190606
PL  - England
TA  - Int J Inj Contr Saf Promot
JT  - International journal of injury control and safety promotion
JID - 101247254
SB  - IM
MH  - Accidents, Traffic/*statistics & numerical data
MH  - Adolescent
MH  - Adult
MH  - Bicycling/injuries
MH  - Child
MH  - Child, Preschool
MH  - Female
MH  - Humans
MH  - Infant
MH  - Injury Severity Score
MH  - Male
MH  - Middle Aged
MH  - Models, Statistical
MH  - Pedestrians/statistics & numerical data
MH  - Risk Factors
MH  - Safety
MH  - Wounds and Injuries/epidemiology/*etiology
MH  - Young Adult
OTO - NOTNLM
OT  - Injury severity
OT  - bicyclist involved crashes
OT  - mixed generalized ordered logit (MGOL) models
OT  - mixed logit models
OT  - pedestrian involved crashes
EDAT- 2019/06/07 06:00
MHDA- 2020/01/18 06:00
CRDT- 2019/06/07 06:00
PHST- 2019/06/07 06:00 [pubmed]
PHST- 2020/01/18 06:00 [medline]
PHST- 2019/06/07 06:00 [entrez]
AID - 10.1080/17457300.2019.1595665 [doi]
PST - ppublish
SO  - Int J Inj Contr Saf Promot. 2019 Sep;26(3):302-314. doi: 
      10.1080/17457300.2019.1595665. Epub 2019 Jun 6.