PMID- 37343458
OWN - NLM
STAT- MEDLINE
DCOM- 20230710
LR  - 20230718
IS  - 1879-2057 (Electronic)
IS  - 0001-4575 (Linking)
VI  - 190
DP  - 2023 Sep
TI  - Bivariate joint analysis of injury severity of drivers in truck-car crashes 
      accommodating multilayer unobserved heterogeneity.
PG  - 107175
LID - S0001-4575(23)00222-1 [pii]
LID - 10.1016/j.aap.2023.107175 [doi]
AB  - Truck-involved crashes, especially truck-car crashes, are associated with serious 
      and even fatal injuries, thus necessitating an in-depth analysis. Prior research 
      focused solely on examining the injury severity of truck drivers or developed 
      separate performance models for truck and car drivers. However, the severity of 
      injuries to both drivers in the same truck-car crash may be interrelated, and 
      influencing factors of injury severities sustained by the two parties may differ. 
      To address these concerns, a random parameter bivariate probit model with 
      heterogeneity in means (RPBPHM) is applied to examine factors affecting the 
      injury severity of both drivers in the same truck-car crash and how these factors 
      change over the years. Using truck-car crash data from 2017 to 2019 in the UK, 
      the dependent variable is defined as slight injury and serious injury or 
      fatality. Factors such as driver, vehicle, road, and environmental 
      characteristics are statistically analyzed in this study. According to the 
      findings, the RPBPHM model demonstrated a remarkable statistical fit, and a 
      positive correlation was observed between the two drivers' injury severity in 
      truck-car crashes. More importantly, the effects of the explanatory factors 
      showing relatively temporal stability vary across different types of vehicle 
      crashes. For example, car driver improper actions and lane changing by trucks, 
      have a significant interactive effect on the severity of injuries sustained by 
      drivers involved collisions between trucks and cars. Male truck drivers, young 
      truck drivers, older truck drivers, and truck drivers' improper actions, elevate 
      the estimated odds of only truck drivers; while older car and unsignalized 
      crossing increase the possibility of injury severity of only car drivers. 
      Finally, due to shared unobserved crash-specific factors, the 30-mph speed limit, 
      dark no lights, and head-on collision, significantly affect the severity of 
      injuries sustained by drivers involved in collisions between trucks and cars. The 
      modeling approach provides a novel framework for jointly analyzing truck-involved 
      crash injury severities. The findings will help policymakers take the necessary 
      actions to reduce truck-car crashes by implementing appropriate and accurate 
      safety countermeasures.
CI  - Copyright (c) 2023 Elsevier Ltd. All rights reserved.
FAU - Song, Dongdong
AU  - Song D
AD  - MOT Key Laboratory of Transport Industry of Big Data Application Technologies for 
      Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China. 
      Electronic address: dongdongsong@bjtu.edu.cn.
FAU - Yang, Xiaobao
AU  - Yang X
AD  - MOT Key Laboratory of Transport Industry of Big Data Application Technologies for 
      Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China. 
      Electronic address: yangxb@bjtu.edu.cn.
FAU - Yang, Yitao
AU  - Yang Y
AD  - MOT Key Laboratory of Transport Industry of Big Data Application Technologies for 
      Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China; 
      Department of Transport & Planning, Faculty of Civil Engineering and Geosciences, 
      Delft University of Technology, Stevinweg1, Delft 2628 CN, the Netherlands. 
      Electronic address: yitao-yang@bjtu.edu.cn.
FAU - Cui, Pengfei
AU  - Cui P
AD  - MOT Key Laboratory of Transport Industry of Big Data Application Technologies for 
      Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China. 
      Electronic address: pengfeicui@bjtu.edu.cn.
FAU - Zhu, Guangyu
AU  - Zhu G
AD  - MOT Key Laboratory of Transport Industry of Big Data Application Technologies for 
      Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China. 
      Electronic address: gyzhu@bjtu.edu.cn.
LA  - eng
PT  - Journal Article
DEP - 20230619
PL  - England
TA  - Accid Anal Prev
JT  - Accident; analysis and prevention
JID - 1254476
SB  - IM
MH  - Male
MH  - Humans
MH  - *Automobiles
MH  - Accidents, Traffic
MH  - Motor Vehicles
MH  - Correlation of Data
MH  - *Wounds and Injuries/epidemiology
MH  - Logistic Models
OTO - NOTNLM
OT  - Bivariate probit model
OT  - Heterogeneity in the means
OT  - Injury severity
OT  - Temporal stability
OT  - Truck-car crashes
OT  - Unobserved heterogeneity
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2023/06/22 01:07
MHDA- 2023/07/10 06:42
CRDT- 2023/06/21 18:06
PHST- 2023/05/03 00:00 [received]
PHST- 2023/06/11 00:00 [revised]
PHST- 2023/06/12 00:00 [accepted]
PHST- 2023/07/10 06:42 [medline]
PHST- 2023/06/22 01:07 [pubmed]
PHST- 2023/06/21 18:06 [entrez]
AID - S0001-4575(23)00222-1 [pii]
AID - 10.1016/j.aap.2023.107175 [doi]
PST - ppublish
SO  - Accid Anal Prev. 2023 Sep;190:107175. doi: 10.1016/j.aap.2023.107175. Epub 2023 
      Jun 19.