Relationship between heavy vehicle periodic inspections, crash contributing factors and crash severity

被引:27
作者
Assemi, Behrang [1 ]
Hickman, Mark [1 ]
机构
[1] Univ Queensland, Sch Civil Engn, Brisbane, Qld 4072, Australia
关键词
Heavy vehicle; Periodic vehicle inspection; Crash contributing factors; Crash severity; Partial least squares path model (PLS-PM); INJURY SEVERITY; ACCIDENTS; SAFETY;
D O I
10.1016/j.tra.2018.04.018
中图分类号
F [经济];
学科分类号
02 ;
摘要
Heavy vehicle crashes are a major contributor to road-related fatalities. Representing only 3% of the total number of registered vehicles and 8% of the total vehicle kilometers traveled, heavy vehicles are involved in 18% of fatal and serious injury crashes in Australia. Given the contributing role of vehicle defects in many heavy vehicle crashes, vehicle inspection schemes have been implemented to more effectively manage heavy vehicle safety. However, there is little empirical research about the impact of periodic heavy vehicle inspections on vehicle defects and crash casualties. Hence, this research investigates the efficacy and effectiveness of periodic heavy vehicle inspections by examining their impact on the factors contributing to heavy vehicle crashes as well as the severity of these crashes. Accordingly, a partial least squares path model (PLSPM) is proposed and evaluated using the data of periodic heavy vehicle inspections and heavy vehicle crashes in Queensland, for the period of 2011-2013. The PLS-PM results are also compared with the results of potential, alternative analysis methods to provide further insights about potential applications of PLS-PM in transportation research. Although the scheme cannot be evaluated completely through the proposed analysis approach, the findings of this study contribute to the causal theory and practice of heavy vehicle inspection protocols, especially in relation to vehicle defects and road safety outcomes.
引用
收藏
页码:441 / 459
页数:19
相关论文
共 48 条
[1]   Why PLS-SEM is suitable for complex modelling? An empirical illustration in big data analytics quality [J].
Akter, Shahriar ;
Wamba, Samuel Fosso ;
Dewan, Saifullah .
PRODUCTION PLANNING & CONTROL, 2017, 28 (11-12) :1011-1021
[2]  
Andreski P., 2009, Panel Study of Income Dynamics Technical Paper Series, P1
[3]  
[Anonymous], 2016, Validity and reliability, DOI DOI 10.1201/B16017-6
[4]  
[Anonymous], 1985, Encyclopedia of Statistical Sciences
[5]  
[Anonymous], 2004, Communications of the Association for Information Systems, DOI [DOI 10.17705/1CAIS.01324, 10.17705/1CAIS.01324]
[6]  
Assemi B., 2016, INT C TRAFF TRANSP P, P1
[7]   Condition of Trucks and Truck Crash Involvement Evidence from the Large Truck Crash Causation Study [J].
Blower, Daniel ;
Green, Paul E. ;
Matteson, Anne .
TRANSPORTATION RESEARCH RECORD, 2010, (2194) :21-28
[8]   Does periodic vehicle inspection reduce car crash injury? Evidence from the Auckland Car Crash Injury Study [J].
Blows, S ;
Ivers, RQ ;
Connor, J ;
Arneratunga, S ;
Norton, R .
AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH, 2003, 27 (03) :323-327
[9]   Factors affecting the severity of work related traffic crashes in drivers receiving a worker's compensation claim [J].
Boufous, Soulfiane ;
Williamson, Ann .
ACCIDENT ANALYSIS AND PREVENTION, 2009, 41 (03) :467-473
[10]  
Chin W. W., 2003, PIS GRAPH 3 0 USERS