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

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 18% in 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 (PLS-PM) 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.

[1]  Jeremy Woolley,et al.  Heavy vehicle road safety: A scan of recent literature , 2011 .

[2]  Wynne W. Chin,et al.  Handbook of Partial Least Squares , 2010 .

[3]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[4]  Sergio R. Jara-Díaz,et al.  Understanding cyclists' perceptions, keys for a successful bicycle promotion , 2014 .

[5]  Cheryl Burke Jarvis,et al.  A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research , 2003 .

[6]  Detmar W. Straub,et al.  Validation Guidelines for IS Positivist Research , 2004, Commun. Assoc. Inf. Syst..

[7]  Moez Limayem,et al.  How Habit Limits the Predictive Power of Intention: The Case of Information Systems Continuance , 2007, MIS Q..

[8]  Detmar W. Straub,et al.  A Practical Guide To Factorial Validity Using PLS-Graph: Tutorial And Annotated Example , 2005, Commun. Assoc. Inf. Syst..

[9]  Hany M. Hassan,et al.  Exploring the risk factors associated with the size and severity of roadway crashes in Riyadh. , 2013, Journal of safety research.

[10]  Geert Wets,et al.  Externality of risk and crash severity at roundabouts. , 2010, Accident; analysis and prevention.

[11]  Daniel Blower,et al.  Condition of Trucks and Truck Crash Involvement , 2010 .

[12]  Rena Friswell,et al.  Safety management for heavy vehicle transport: A review of the literature , 2014 .

[13]  Brendan J. Russo,et al.  Comparison of factors affecting injury severity in angle collisions by fault status using a random parameters bivariate ordered probit model , 2014 .

[14]  Ann Williamson,et al.  Factors affecting the severity of work related traffic crashes in drivers receiving a worker's compensation claim. , 2009, Accident; analysis and prevention.

[15]  Stuart Newstead,et al.  An evaluation of costs and benefits of a vehicle periodic inspection scheme with six-monthly inspections compared to annual inspections. , 2013, Accident; analysis and prevention.

[16]  Rune Elvik,et al.  Effects on accidents of periodic motor vehicle inspection in Norway. , 2007, Accident; analysis and prevention.

[17]  Sebastian Seebauer,et al.  Technology adoption of electric bicycles: A survey among early adopters , 2014 .

[18]  Mohammed A Quddus,et al.  Injury severity analysis of accidents involving young male drivers in Great Britain. , 2008, Journal of safety research.

[19]  Jacob Cohen Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[20]  Younshik Chung,et al.  Deep subterranean railway system: Acceptability assessment of the public discourse in the Seoul Metropolitan Area of South Korea , 2015 .

[21]  J. Hair Multivariate data analysis : a global perspective , 2010 .

[22]  Zhang Yang,et al.  Exploring contributing factors to crash injury severity at freeway diverge areas using ordered probit model , 2011 .

[23]  Jennie Connor,et al.  Does periodic vehicle inspection reduce car crash injury? Evidence from the Auckland Car Crash Injury Study , 2003, Australian and New Zealand journal of public health.

[24]  Adamantios Diamantopoulos,et al.  Advancing formative measurement models , 2008 .

[25]  S. Washington,et al.  Statistical and Econometric Methods for Transportation Data Analysis , 2010 .

[26]  Shahriar Akter,et al.  Why PLS-SEM is suitable for complex modeling? An empirical illustration in Big Data Analytics Quality , 2017 .

[27]  Hjp Harry Timmermans,et al.  Information impact on quality of multimodal travel choices: conceptualizations and empirical analyses , 2007 .

[28]  Nils Urbach,et al.  Structural Equation Modeling in Information Systems Research Using Partial Least Squares , 2010 .

[29]  J. Hair Multivariate data analysis , 1972 .

[30]  Michel Tenenhaus,et al.  PLS path modeling , 2005, Comput. Stat. Data Anal..

[31]  Marko Sarstedt,et al.  Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research , 2014 .

[32]  Detmar W. Straub,et al.  Specifying Formative Constructs in Information Systems Research , 2007, MIS Q..

[33]  Thomas F. Golob,et al.  Structural Equation Modeling For Travel Behavior Research , 2001 .

[34]  Rune Elvik The effect on accidents of technical inspections of heavy vehicles in Norway. , 2002, Accident; analysis and prevention.