Abstract
Road freight transport and heavy vehicles play a significant role in transporting commodities and merchandise goods in Australia. According to the predictions, heavy vehicle traffic in Australia will increase by 50 per cent by 2030. The heavy vehicle traffic growth increases safety concerns since the probability of vehicle crashes increases by five per cent when the heavy vehicle percentage is higher than 30 per cent of the total traffic volume. Despite comprising a small proportion of total registered vehicles (roughly 3%), heavy vehicles are involved in 18% of total road fatalities. Therefore, the reduction in the number of crashes involving heavy vehicles is of high priority. The objective of this research is to identify the influencing factors on injury severity in heavy vehicle crashes using the multinomial logit model. A dataset, including 9175 heavy vehicle crashes in the Victoria State, Australia, over the period of 2006–2018 is used in this paper. The severity of truck crashes includes fatal/serious injury, minor injury, and property damage only, and various characteristics were examined as the potential risks factor, including the driver, crash, road, environment, and vehicle characteristics. Our results show that factors such as occupant variables (e.g. driver age and gender), collision characteristics (e.g. collisions with fixed objects and truck overturns), temporal characteristics (e.g. early morning, midnight), spatial characteristics (e.g. urban or rural areas), environmental factors (e.g. lights condition) increase the probability of fatal/serious injuries in heavy vehicle crashes. The result from the study can be applied to reduce the severity of heavy vehicle crashes.
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Data availability
The data used in this study are available at the crash database hosted by VicRoads, Victoria.
References
Abrari Vajari M, Aghabayk K, Sadeghian M, Shiwakoti N (2020) A multinomial logit model of motorcycle crash severity at Australian intersections. J Safety Res 73:17–24. https://doi.org/10.1016/j.jsr.2020.02.008
Aghabayk K, Sarvi M, Young W (2014) Attribute selection for modelling driver’s car-following behaviour in heterogeneous congested traffic conditions. Trans A: Trans Sci 10(5):457–468. https://doi.org/10.1080/23249935.2013.787558
Aghabayk K, Sarvi M, Young W (2015) A state-of-the-art review of car-following models with particular considerations of heavy vehicles. Transp Rev 35(1):82–105. https://doi.org/10.1080/01441647.2014.997323
Alam K, Mahal A (2016) The economic burden of road traffic injuries on households in South Asia. PLoS ONE 11(10):1–16. https://doi.org/10.1371/journal.pone.0164362
Al-Balbissi AH (2003) Role of gender in road accidents. Traffic Inj Prev 4(1):64–73. https://doi.org/10.1080/15389580309857
Al-Bdairi NSS, Hernandez S (2017) An empirical analysis of run-off-road injury severity crashes involving large trucks. Accid Anal Prev 102:93–100. https://doi.org/10.1016/j.aap.2017.02.024
Awadzi KD, Classen S, Hall A, Duncan RP, Garvan CW (2008) Predictors of injury among younger and older adults in fatal motor vehicle crashes. Accid Anal Prev 40(6):1804–1810. https://doi.org/10.1016/j.aap.2008.07.010
Azimi G, Rahimi A, Asgari H, Jin X (2020) Severity analysis for large truck rollover crashes using a random parameter ordered logit model. Accid Anal Prev 135:105355. https://doi.org/10.1016/j.aap.2019.105355
Balakrishnan S, Moridpour S, Tay R (2016) Differences in single heavy vehicle crashes at intersections and midblocks. J Adv Transp 50(8):2150–2159. https://doi.org/10.1002/atr.1451
Balakrishnan S, Moridpour S, Tay R (2019) Sociodemographic influences on injury severity in truck-vulnerable road user crashes. J Risk Uncertain Eng Syst, Part A: Civil Eng 5(4):4019015. https://doi.org/10.1061/AJRUA6.0001023
Behnood A, Mannering F (2019) Time-of-day variations and temporal instability of factors affecting injury severities in large-truck crashes. Anal Method Accid Res 23:100102. https://doi.org/10.1016/j.amar.2019.100102
Bose D, Segui-Gomez M, Crandall JR (2011) Vulnerability of female drivers involved in motor vehicle crashes: an analysis of US population at risk. Am J Public Health 101(12):2368–2373. https://doi.org/10.2105/AJPH.2011.300275
Çelik AK, Oktay E (2014) A multinomial logit analysis of risk factors influencing road traffic injury severities in the Erzurum and Kars Provinces of Turkey. Accid Anal Prev 72:66–77. https://doi.org/10.1016/j.aap.2014.06.010
Cerwick DM, Gkritza K, Shaheed MS, Hans Z (2014) A comparison of the mixed logit and latent class methods for crash severity analysis. Anal Methods Accid Res 3–4:11–27. https://doi.org/10.1016/j.amar.2014.09.002
Chang L-Y, Chien J-T (2013) Analysis of driver injury severity in truck-involved accidents using a non-parametric classification tree model. Saf Sci 51(1):17–22. https://doi.org/10.1016/j.ssci.2012.06.017
Chang L-Y, Mannering F (1999) Analysis of injury severity and vehicle occupancy in truck- and non-truck-involved accidents. Accid Anal Prev 31(5):579–592. https://doi.org/10.1016/S0001-4575(99)00014-7
Cole NB, Barrett SM, Bolding MC, Aust WM (2019) An analysis of fatal log truck crashes in the United States from 2011 through 2015. Int J for Eng 30(2):121–131. https://doi.org/10.1080/14942119.2018.1564964
Crash statistics : VicRoads. (n.d.). Retrieved October 2, 2018, from https://www.vicroads.vic.gov.au/safety-and-road-rules/safety-statistics/crash-statistics
Deffenbacher JL, Lynch RS, Filetti LB, Dahlen ER, Oetting ER (2003) Anger, aggression, risky behavior, and crash-related outcomes in three groups of drivers. Behav Res Ther 41(3):333–349. https://doi.org/10.1016/s0005-7967(02)00014-1
Dong B, Ma X, Chen F, Chen S (2018) Investigating the differences of single-vehicle and multivehicle accident probability using mixed logit model. J Adv Transp 2018:2702360. https://doi.org/10.1155/2018/2702360
Duncan CS, Khattak AJ, Council FM (1998) Applying the ordered probit model to injury severity in truck-passenger car rear-end collisions. Transp Res Rec 1635:63–71. https://doi.org/10.3141/1635-09
Geedipally SR, Turner PA, Patil S (2011) Analysis of motorcycle crashes in texas with multinomial logit model. Transp Res Rec: J Transp Res Board 2265(1):62–69. https://doi.org/10.3141/2265-07
Ghasemi Noughabi M, Sadeghi A, Mohammadzadeh Moghaddam A, Jalili Qazizadeh M (2020) Fatigue risk management: assessing and ranking the factors affecting the degree of fatigue and sleepiness of heavy-vehicle drivers using TOPSIS and statistical analyses. Iran J Sci Technol, Trans Civil Eng 44(4):1345–1357. https://doi.org/10.1007/s40996-019-00320-9
Global status report on road safety. (2018). Geneva: world health organization. Licence: CC BY- NC-SA 3.0 IGO.
Hao W, Kamga C, Yang X, Ma J, Thorson E, Zhong M, Wu C (2016) Driver injury severity study for truck involved accidents at highway-rail grade crossings in the United States. Transp Res F: Traffic Psychol Behav 43:379–386. https://doi.org/10.1016/j.trf.2016.09.001
Hausman J, McFadden D (1984) Specification tests for the multinomial logit model. Econometrica 52(5):1219–1240
Islam M, Hernandez S (2013) Large truck-involved crashes: exploratory injury severity analysis. J Transp Eng 139(6):596–604. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000539
Islam S, Mannering F (2006) Driver aging and its effect on male and female single-vehicle accident injuries: some additional evidence. J Safety Res 37(3):267–276. https://doi.org/10.1016/j.jsr.2006.04.003
Khorashadi A, Niemeier D, Shankar V, Mannering F (2005) Differences in rural and urban driver-injury severities in accidents involving large-trucks: an exploratory analysis. Accid Anal Prev 37(5):910–921. https://doi.org/10.1016/j.aap.2005.04.009
Lin M-R, Huang W, Hwang H-F, Wu H-DI, Yen L-L (2004) The effect of crash experience on changes in risk taking among urban and rural young people. Accid Anal Prev 36(2):213–222. https://doi.org/10.1016/S0001-4575(02)00150-1
Litchfield, F. (2017). The cost of road crashes in Australia 2016: an overview of safety strategies. Retrieved from https://www.aph.gov.au/DocumentStore.ashx?id=a37c13ee-72d4-47a9-904b-360d3e635caa
Liu P, Fan W, (David). (2020) Analyzing injury severity of rear-end crashes involving large trucks using a mixed logit model: a case study in North Carolina. J Transp Safety Secur. https://doi.org/10.1080/19439962.2020.1812784
Moridpour S, Sarvi M, Rose G (2010) Lane changing models: a critical review. Transp Lett 2(3):157–173. https://doi.org/10.3328/TL.2010.02.03.157-173
Moridpour S, Mazloumi E, Mesbah M (2015) Impact of heavy vehicles on surrounding traffic characteristics. J Adv Transp 49(4):535–552. https://doi.org/10.1002/atr.1286
Nantulya VM, Reich MR (2002) The neglected epidemic: road traffic injuries in developing countries. BMJ (clin Res Ed) 324(7346):1139–1141. https://doi.org/10.1136/bmj.324.7346.1139
Pahukula J, Hernandez S, Unnikrishnan A (2015) A time of day analysis of crashes involving large trucks in urban areas. Accid Anal Prev 75:155–163. https://doi.org/10.1016/j.aap.2014.11.021
Rahimi E, Shamshiripour A, Samimi A, Mohammadian A, (Kouros). (2020) Investigating the injury severity of single-vehicle truck crashes in a developing country. Accid Anal Prev 137:105444. https://doi.org/10.1016/j.aap.2020.105444
Rezapour M, Wulff SS, Ksaibati K (2019) Truck crashes and potential countermeasures on Wyoming highways and interstates: recommendations for all responsible agencies. J Transp Safety Secur. https://doi.org/10.1080/19439962.2019.1638477
Salum JH, Kitali AE, Bwire H, Sando T, Alluri P (2019) Severity of motorcycle crashes in Dar es Salaam Tanzania. Traffic Injury Prev 20(2):189–195. https://doi.org/10.1080/15389588.2018.1544706
Samerei SA, Aghabayk K, Mohammadi A, Shiwakoti N (2020) Data mining approach to model bus crash severity in Australia. J Safety Res. https://doi.org/10.1016/j.jsr.2020.12.004
Savolainen P, Mannering F (2007) Probabilistic models of motorcyclists’ injury severities in single- and multi-vehicle crashes. Accid Anal Prev 39(5):955–963. https://doi.org/10.1016/j.aap.2006.12.016
Savolainen PT, Mannering FL, Lord D, Quddus MA (2011) The statistical analysis of highway crash-injury severities: a review and assessment of methodological alternatives. Accid Anal Prev 43(5):1666–1676. https://doi.org/10.1016/j.aap.2011.03.025
Smith, K. (2000). Road safety: past, present and future, paper presented to australian institute of traffic planning and management. In: (AITPM) International Conference. Gold Coast, Queensland, Australia.
Stein HS, Jones IANS (1988) Crash involvement of large trucks by configuration : a case-control study. Am J Public Health 78(5):491–498
Tavakoli Kashani A, Amirifar S, Azizi Bondarabadi M (2020) Analysis of driver and vehicle characteristics involved in red-light running crashes: Isfahan Iran. Iran J Sci Technol, Trans Civil Eng 45(1):381–387. https://doi.org/10.1007/s40996-020-00453-2
Taylor SG, Russo BJ, James E (2018) A comparative analysis of factors affecting the frequency and severity of freight-involved and non-freight crashes on a major freight corridor freeway. Transp Res Rec 2672(34):49–62. https://doi.org/10.1177/0361198118776815
Toran Pour A, Moridpour S, Tay R, Rajabifard A (2017a) Modelling pedestrian crash severity at mid-blocks. Transp A: Transp Sci 13(3):273–297. https://doi.org/10.1080/23249935.2016.1256355
Toran Pour A, Moridpour S, Tay R, Rajabifard A (2017b) Neighborhood Influences on vehicle-pedestrian crash severity. J Urban Health 94(6):855–868. https://doi.org/10.1007/s11524-017-0200-z
Toran Pour A, Moridpour S, Tay R, Rajabifard A (2018) Influence of pedestrian age and gender on spatial and temporal distribution of pedestrian crashes. Traffic Inj Prev 19(1):81–87. https://doi.org/10.1080/15389588.2017.1341630
Trends Infrastructure and Transport to 2030 (2014) Department of infrastructure and regional development, Canberra, ACT, Australia
Uddin M, Huynh N (2017) Truck-involved crashes injury severity analysis for different lighting conditions on rural and urban roadways. Accid Anal Prev 108:44–55. https://doi.org/10.1016/j.aap.2017.08.009
Uddin M, Huynh N (2018) Factors influencing injury severity of crashes involving HAZMAT trucks. Int J Trans Sci Technol 7(1):1–9. https://doi.org/10.1016/j.ijtst.2017.06.004
Uddin M, Huynh N (2020) Injury severity analysis of truck-involved crashes under different weather conditions. Accid Anal Prev 141:105529. https://doi.org/10.1016/j.aap.2020.105529
Wang Y, Luo Y, Chen F (2019) Interpreting risk factors for truck crash severity on mountainous freeways in Jiangxi and Shaanxi China. Eur Transp Res Rev 11(1):26. https://doi.org/10.1186/s12544-019-0366-4
Washington P, Karlaftis G, Mannering F (2011) Statistical and econometric methods for transportation data analysis. Taylor & Francis, New York, NY
Yan X, Radwan E, Abdel-Aty M (2005) Characteristics of rear-end accidents at signalized intersections using multiple logistic regression model. Accid Anal Prev 37(6):983–995. https://doi.org/10.1016/j.aap.2005.05.001
Ye F, Lord D (2014) Investigation of effects of underreporting crash data on three commonly used traffic crash severity models multinomial logit ordered probit and mixed logit. Transp Res Rec, 2241 2241(1):51–58. https://doi.org/10.3141/2241-06
Yu M, Ma C, Zheng C, Chen Z, Yang T (2020) Injury severity of truck-involved crashes in work zones on rural and urban highways: accounting for unobserved heterogeneity. J Transp Safety Secur. https://doi.org/10.1080/19439962.2020.1726544
Zeng Q, Wen H, Huang H, Pei X, Wong SC (2018) Incorporating temporal correlation into a multivariate random parameters Tobit model for modeling crash rate by injury severity. Transp A: Transp Sci 14(3):177–191. https://doi.org/10.1080/23249935.2017.1353556
Zeng Q, Guo Q, Wong SC, Wen H, Huang H, Pei X (2019) Jointly modeling area-level crash rates by severity: a Bayesian multivariate random-parameters spatio-temporal Tobit regression. Transp A: Transp Sci 15(2):1867–1884. https://doi.org/10.1080/23249935.2019.1652867
Zheng Z, Lu P, Lantz B (2018) Commercial truck crash injury severity analysis using gradient boosting data mining model. J Safety Res 65:115–124. https://doi.org/10.1016/j.jsr.2018.03.002
Zhu X, Srinivasan S (2011) A comprehensive analysis of factors influencing the injury severity of large-truck crashes. Accid Anal Prev 43(1):49–57. https://doi.org/10.1016/j.aap.2010.07.007
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Abrari Vajari, M., Aghabayk, K., Sadeghian, M. et al. Modelling the Injury Severity of Heavy Vehicle Crashes in Australia. Iran J Sci Technol Trans Civ Eng 46, 1635–1644 (2022). https://doi.org/10.1007/s40996-021-00673-0
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DOI: https://doi.org/10.1007/s40996-021-00673-0