Looking back in the rearview: Insights into Queensland’s rear-end crashes

References

• Adanu EK, Jones S, Abhay L, Rahman M. 2022. Examining the who, what, and how of risky driving related crashes in residential areas. J Urban Mobility. 2:100024. doi:10.1016/j.urbmob.2022.100024.
   Google Scholar
• Åkerstedt T, Kecklund G, Hörte L-G. 2001. Night driving, season, and the risk of highway accidents. Sleep. 24(4):401–406. doi:10.1093/sleep/24.4.401.
   PubMed Web of Science ®Google Scholar
• Beck D. 2015. Investigation of key crash types: rear-end crashes in urban and rural environments. Sydney, Australia: Austroads.
   Google Scholar
• Budd L, Newstead S, Stephens A. 2020. A real-world evaluation of autonomous emergency braking and forward collision warning in Australasian light vehicles. Accident Research Centre, Monash University.
   Google Scholar
• Cairns J, Warren J, Garthwaite K, Greig G, Bambra C. 2015. Go slow: an umbrella review of the effects of 20 mph zones and limits on health and health inequalities. J Public Health. 37(3):515–520. doi:10.1093/pubmed/fdu067.
   Google Scholar
• Champahom T, Jomnonkwao S, Chatpattananan V, Karoonsoontawong A, Ratanavaraha V. 2019. Analysis of rear-end crash on Thai highway: decision tree approach. J Adv Transp. 2019:1–13. doi:10.1155/2019/2568978.
   Web of Science ®Google Scholar
• Champahom T, Jomnonkwao S, Watthanaklang D, Karoonsoontawong A, Chatpattananan V, Ratanavaraha V. 2020. Applying hierarchical logistic models to compare urban and rural roadway modeling of severity of rear-end vehicular crashes. Accid Anal Prev. 141:105537. doi:10.1016/j.aap.2020.105537.
   PubMed Web of Science ®Google Scholar
• Chen GX, Amandus HE, Wu N. 2014. Occupational fatalities among driver/sales workers and truck drivers in the United States, 2003–2008. Am J Ind Med. 57(7):800–809. doi:10.1002/ajim.22320.
   PubMed Web of Science ®Google Scholar
• Cicchino JB, Zuby DS. 2019. Characteristics of rear-end crashes involving passenger vehicles with automatic emergency braking. Traffic Inj Prev. 20(sup1):S112–S118. doi:10.1080/15389588.2019.1576172.
   PubMed Web of Science ®Google Scholar
• De Rome L, Senserrick T. 2011. Factors associated with motorcycle crashes in New South Wales, Australia, 2004 to 2008. Transp Res Rec. 2265(1):54–61. doi:10.3141/2265-06.
   Google Scholar
• Doecke SD, Kloeden CN, Dutschke JK, Baldock MR. 2018. Safe speed limits for a safe system: the relationship between speed limit and fatal crash rate for different crash types. Traffic Inj Prev. 19(4):404–408. doi:10.1080/15389588.2017.1422601.
   PubMed Web of Science ®Google Scholar
• 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(1):63–71. doi:10.3141/1635-09.
   Google Scholar
• Feng G, Wang W, Feng J, Tan H, Li F. 2010. Modelling and simulation for safe following distance based on vehicle braking process. Paper presented at 2010 IEEE 7th International Conference on E-Business Engineering; Sydney, Australia.
   Google Scholar
• Fildes B, Keall M, Bos N, Lie A, Page Y, Pastor C, Pennisi L, Rizzi M, Thomas P, Tingvall C. 2015. Effectiveness of low speed autonomous emergency braking in real-world rear-end crashes. Accid Anal Prev. 81:24–29. doi:10.1016/j.aap.2015.03.029.
   PubMed Web of Science ®Google Scholar
• Fraser T. 2021. New highway cameras to target ‘dangerous’ tailgaters. Which Car.
   Google Scholar
• Frith W, Harte D. 1986. The safety implications of some control changes at urban intersections. Accid Anal Prev. 18(3):183–192. doi:10.1016/0001-4575(86)90001-1.
   PubMed Web of Science ®Google Scholar
• Giles MJ. 2003. Correcting for selectivity bias in the estimation of road crash costs. Appl Econ. 35(11):1291–1301. doi:10.1080/0003684032000090717.
   Web of Science ®Google Scholar
• Hossain MM, Rahman MA, Sun X, Mitran E. 2021. Investigating underage alcohol-intoxicated driver crash patterns in Louisiana. Transp Res Rec. 2675(11):769–782. doi:10.1177/03611981211019742.
   Web of Science ®Google Scholar
• Islam MT. 2016. Investigating the speed and rear-end collision relationship at urban signalized intersections. Transp Res Rec. 2601(1):10–16. doi:10.3141/2601-02.
   Google Scholar
• Johansson R. 2009. Vision zero–implementing a policy for traffic safety. Saf Sci. 47(6):826–831. doi:10.1016/j.ssci.2008.10.023.
   Web of Science ®Google Scholar
• Khattak AJ. 2001. Injury severity in multivehicle rear-end crashes. Transp Res Rec. 1746(1):59–68. doi:10.3141/1746-08.
   Google Scholar
• Kloeden C, Woolley J, McLean A. 2004. Evaluation of the 50km/h default urban speed limit in South Australia. Paper presented at: 2004 Road Safety Research, Policing and Education Conference, Road Safety Council; Perth, Australia.
   Google Scholar
• Lawrence B, Fildes B, Thompson L, Cook J, Newstead S. 2020. Evaluation of the 30km/h speed limit trial in the City of Yarra, Melbourne, Australia. Traffic Inj Prev.21(sup1):S96–S101. doi:10.80/15389588.2021.1895990.
   Google Scholar
• Li Z, Liu P, Wang W, Xu C. 2014. Development of a control strategy of variable speed limits to reduce rear-end collision risks near freeway recurrent bottlenecks. IEEE Trans Intell Transp Syst. 15(2):866–877.
   Web of Science ®Google Scholar
• Mohamed SA, Mohamed K, Al-Harthi HA. 2017. Investigating factors affecting the occurrence and severity of rear-end crashes. Transp Res Procedia. 25:2098–2107. doi:10.1016/j.trpro.2017.05.403.
   Google Scholar
• Motor Accident Insurance Commission. 2021. CTP scheme overview. [accessed]. https://maic.qld.gov.au/scheme-knowledge-centre/ctp-scheme/.
   Google Scholar
• Ochenasek M, Truelove V, Stefanidis KB, Watson-Brown N. 2022. Examining the impact of both legal and nonlegal factors on following a vehicle too closely utilizing three deterrence-based theories. J Criminol. 55(1):65–80. doi:10.1177/26338076211065208.
   Web of Science ®Google Scholar
• Pilkington P. 2000. Reducing the speed limit to 20 mph in urban areas: child deaths and injuries would be decreased. BMJ. 320(7243):1160–1160. doi:10.1136/bmj.320.7243.1160.
   PubMedGoogle Scholar
• Polders E, Cornu J, De Ceunynck T, Daniels S, Brijs K, Brijs T, Hermans E, Wets G. 2015. Drivers’ behavioral responses to combined speed and red light cameras. Accid Anal Prev. 81:153–166. doi:10.1016/j.aap.2015.05.006.
   PubMed Web of Science ®Google Scholar
• Polus A. 1985. Driver behaviour and accident records at unsignalized urban intersections. Accid Anal Prev. 17(1):25–32. doi:10.1016/0001-4575(85)90005-3.
   PubMed Web of Science ®Google Scholar
• Queensland Government. 2022a. Guide to speed management. In: Roads TaM, editor.
   Google Scholar
• Queensland Government. 2022b. State controlled road length—total rural lane kilometres. Open Data Portal; [accessed 2023 May 10]. https://www.data.qld.gov.au/dataset/state-controlled-road-length-total-rural-lane-kilometres/resource/9ffc1b7f-fca9-48c5-8669-68c5452065b1.
   Google Scholar
• R Core Team. 2022. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.R-project.org.
   Google Scholar
• Rakotonirainy A, Demmel S, Watson A, Haque MM, Fleiter JJ, Watson BC, Washington S. 2017. Prevalence and perception of following too close in Queensland. Paper presented at: Australasian Road Safety Conference 2017 (ACRS2017); Perth, Australia.
   Google Scholar
• Seacrist T, Belwadi A, Prabahar A, Chamberlain S, Megariotis J, Loeb H. 2016. Comparison of crash rates and rear-end striking crashes among novice teens and experienced adults using the shrp2 naturalistic driving study. Traffic Inj Prev. 17(sup1):48–52. doi:10.1080/15389588.2016.1188384.
   PubMed Web of Science ®Google Scholar
• Sharafeldin M, Farid A, Ksaibati K. 2022. Injury severity analysis of rear-end crashes at signalized intersections. Sustainability. 14(21):13858. doi:10.3390/su142113858.
   Web of Science ®Google Scholar
• Slikboer R, Muir SD, Silva SSM, Meyer D. 2020. A systematic review of statistical models and outcomes of predicting fatal and serious injury crashes from driver crash and offense history data. Syst Rev. 9(1):220–220. doi:10.1186/s13643-020-01475-7.
   PubMed Web of Science ®Google Scholar
• Stamatiadis N, Deacon JA. 1997. Quasi-induced exposure: methodology and insight. Accid Anal Prev. 29(1):37–52. doi:10.1016/s0001-4575(96)00060-7.
   PubMed Web of Science ®Google Scholar
• Stephens AN, Stephan KL, Crotty R, O'Hern S, Björklund G. 2023. Too close for comfort: a mixed methods study to understand self-reported tailgating using the theory of planned behaviour. Transp Res Part F Traffic Psychol Behav. 93:11–22. doi:10.1016/j.trf.2022.12.005.
   Web of Science ®Google Scholar
• Sullivan JM, Flannagan NJ. 2003. Risk of fatal rear-end collisions: is there more to it than attention?. Paper presented at: Driving Assessment Conference, University of Iowa; Park City, UT. doi:10.17077/drivingassessment.1130.
   Google Scholar
• Therneau T, Atkinson B. 2022. rpart: Recursive partitioning and regression trees. https://CRAN.R-project.org/package=rpart.
   Google Scholar
• Transport and Main Roads. 2023. Crash data from Queensland roads [accessed 2023 Feb 14]. https://www.data.qld.gov.au/dataset/crash-data-from-queensland-roads.
   Google Scholar
• Wang X, Abdel-Aty M. 2006. Temporal and spatial analyses of rear-end crashes at signalized intersections. Accid Anal Prev. 38(6):1137–1150. doi:10.1016/j.aap.2006.04.022.
   PubMed Web of Science ®Google Scholar
• Wang Y-g, Chen K-m, Hu L-w. 2012. Killer tailgating: recommendation of traveling intervals between consecutive motor vehicles for rear-end collision avoidance. Arab J Sci Eng. 37(3):619–630. doi:10.1007/s13369-012-0200-y.
   Web of Science ®Google Scholar
• Weng J, Yang D, Chai T, Fu S. 2019. Investigation of occurrence likelihood of human errors in shipping operations. Ocean Eng. 182:28–37. doi:10.1016/j.oceaneng.2019.04.083.
   Web of Science ®Google Scholar
• Wiacek C, Bean J, Sharma D. 2015. Real-world analysis of fatal rear-end crashes. Paper presented at: 24th International Technical Conference on the Enhanced Safety of Vehicles (ESV) National Highway Traffic Safety Administration; Gothenburg, Sweden.
   Google Scholar
• World Bank. 2022. Urban population (% of total population)—Australia; [accessed 2023]. https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS?end=2021&locations=AU&start=1960&view=map.
   Google Scholar
• Yan X, Radwan E. 2006. Analyses of rear-end crashes based on classification tree models. Traffic Inj Prev. 7(3):276–282. doi:10.1080/15389580600660062.
   PubMedGoogle Scholar
• 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. doi:10.1016/j.aap.2005.05.001.
   PubMed Web of Science ®Google Scholar
• Yue L, Abdel-Aty MA, Wu Y, Farid A. 2020. The practical effectiveness of advanced driver assistance systems at different roadway facilities: system limitation, adoption, and usage. IEEE Trans Intell Transport Syst. 21(9):3859–3870. doi:10.1109/TITS.2019.2935195.
   Web of Science ®Google Scholar
• Zellmer TJ, Freeman PT, Wagner JR, Alexander KE, Pidgeon P. 2014. A mobile tailgating detection system for law enforcement surveillance. Paper presented at: Dynamic Systems and Control Conference; San Antonio, TX. doi:10.1115/DSCC2014-5884.
   Google Scholar
• Zhao S, Wang K, Jackson E. 2019. Evaluation of not-at-fault assumption in quasi-induced exposure method using traffic crash data at varied geographical levels. Transp Res Rec. 2673(4):593–604. doi:10.1177/0361198119841036.
   Web of Science ®Google Scholar