Injury severity prediction model for two-wheeler crashes at mid-block road sections

References

• MORTH report. [cited 2019 May]. Available from: https://morth.nic.in/road-accident-in-india
   Google Scholar
• Bogue S, Paleti R, Balan L. A modified rank-ordered logit model to analyse injury severity of occupants in multi-vehicle crashes. Anal Methods Accid Res. 2017;14:22–40.
   Web of Science ®Google Scholar
• Abdel-Aty M. Analysis of driver injury severity levels at multiple locations using ordered probit models. J Safety Res. 2003;34(5):597–603.
   PubMed Web of Science ®Google Scholar
• Quddus MA, Noland RB, Chin HC. An analysis of motorcycle injury and vehicle damage severity using ordered probit models. J Safety Res. 2002;33(4):445–462.
   PubMed Web of Science ®Google Scholar
• Allen T, Newstead S, Lenne MG, et al. Contributing factors to motorcycle injury crashes in Victoria, Australia. Transp Res Part F Traffic Psycol Behav. 2017;45:157–168.
   Web of Science ®Google Scholar
• Maestracci M, Prochasson F, Geffroy A, et al. Powered two-wheelers road accidents and their risk perception in dense urban areas: case of Paris. Accid Anal Prev. 2012;49:114–123.
   PubMed Web of Science ®Google Scholar
• Casifo S, Cava GL, Pappalardo G. A logistic model for powered two-wheelers crash in Italy. Procedia Soc Behav Sci. 2012;53:881–890.
   Google Scholar
• Chang F, Li M, Xu P, et al. Injury severity of motorcycle riders involved in traffic crashes in Hunan, China: a mixed ordered logit approach. Int J Environ Res Public Health. 2016;13(7):714.
   PubMed Web of Science ®Google Scholar
• Islam S, Brown J. A comparative injury severity analysis of motorcycle at-fault crashes on rural and urban roadways in Alabama. Accid Anal Prev. 2017;108:163–171.
   PubMed Web of Science ®Google Scholar
• Wahab L, Jiang H. Severity prediction of motorcycle crashes with machine learning methods. Int J Crashworthiness. 2019;1–8.
   Web of Science ®Google Scholar
• Montella A, Ona R, Mauriello F, et al. A data mining approach to investigate patterns of powered two-wheeler crashes in Spain. Accid Anal Prev. 2019;134:105251.
   PubMed Web of Science ®Google Scholar
• Pruthi N, Chandramouli BA, Sampath S, et al. Patterns of head injury among drivers and pillion riders of motorised two-wheeled vehicles in Banglore. Indian J Neurotrauma. 2010;7(2):123–127.
   Google Scholar
• Naqvi HM, Tiwari G. Factors contributing to motorcycle fatal crashes on national highways in India. Transp Res Procedia. 2017;25:2084–2097.
   Google Scholar
• Kumar S, Toshniwal D. Severity of powered two-wheeler traffic accidents in Uttarakhand, India. Eur Transp Res Rev. 2017;9(2):24.
   Web of Science ®Google Scholar
• Chen C, Zhang G, Yang J, et al. An explanatory analysis of driver injury severity in rear-end crashes using a decision table/Naïve Bayes (DTNB) hybrid classifier. Accid Anal Prev. 2016;90:95–107.
   PubMed Web of Science ®Google Scholar
• Shankar V, Mannering F. An exploratory multinomial logit analysis of single-vehicle motorcycle accident severity. J Saf Res. 1996;27(3):183–194.
   Web of Science ®Google Scholar
• Lee C, Li X. Predicting driver injury severity in single-vehicle and two-vehicle crashes with boosted regression trees. Transp Res Rec. 2015;2514(1):138–148.
   Google Scholar
• Li Z, Liu P, Wang W, et al. Using support vector machine models for crash injury severity analysis. Accid Anal Prev. 2012;45:478–486.
   PubMed Web of Science ®Google Scholar
• Zhang J, Li Z, Pu Z, et al. Comparing prediction performance for crash injury severity among various machine learning and statistical methods. IEEE Access 2018;6:60079–60087.
   Google Scholar
• Abdel-Aty MA, Abdelwahab HT. Predicting injury severity levels in traffic crashes: modelling comparison. J Transp Eng. 2004;130(2):204–210.
   Web of Science ®Google Scholar
• Iranitalab A, Khattak A. Comparison of four statistical and machine learning methods for crash severity prediction. Accid Anal Prev. 2017;108:27–36.
   PubMed Web of Science ®Google Scholar
• Li D, Ranjitkar P, Zhao Y, et al. Analysing pedestrian crash injury severity under different weather conditions. Traffic Inj Prev. 2017;18(4):427–430.
   PubMed Web of Science ®Google Scholar
• Theofilatos A. Incorporating real-time traffic and weather data to explore road accident likelihood and severity in urban arterials. J Safety Res. 2017;61:9–21.
   PubMed Web of Science ®Google Scholar
• Janitza S, Tutz G, Boulesteix A. Random forest for ordinal responses: prediction and variable selection. Comput Stat Data Anal. 2016;96:57–73.
   Web of Science ®Google Scholar
• Das A, Abdel-Aty M, Pande A. Using conditional inference forests to identify the factors affecting crash severity on arterial corridors. J Safety Res. 2009;40(4):317–327.
   PubMed Web of Science ®Google Scholar
• Duncan CS, Khattak AJ, Council FM. Applying the ordered probit model to injury severity in truck-passenger car rear-end collisions. Transp Res Rec. 1998;1635(1):63–71.
   Google Scholar
• Garrido R, Bastos A, de Almeida A, et al. Prediction of road accident severity using the ordered probit model. Transp Res Procedia. 2014;3:214–223.
   Google Scholar
• Strobl C, Boulesteix A, Zeileis A, et al. Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinformatics. 2007;8:25.
   PubMed Web of Science ®Google Scholar
• Jeong H, Jang Y, Bowman PJ, et al. Classification of motor vehicle crash injury severity: a hybrid approach for imbalanced data. Accid Anal Prev. 2018;120:250–261.
   PubMed Web of Science ®Google Scholar
• Jiang L, Xie Y, Ren T. Modeling highly unbalanced crash injury severity data by 4 ensemble methods and global sensitivity analysis. Transportation Research Board 98th Annual Meeting, Washington, DC, USA; 2019.
   Google Scholar
• Degenhardt F, Seifert S, Szymczak S. Evaluation of variable selection methods for random forests and omics data sets. Brief Bioinformatics. 2019;20(2):492–503.
   PubMed Web of Science ®Google Scholar
• Sarda-Espinosa A, Subbiah S, Bartz-Beielstein T. Conditional inference trees for knowledge extraction from motor health condition data. Eng Appl Artif Intell. 2017;62:26–37.
   Web of Science ®Google Scholar
• Kuhn S, Lisofsky N, Banaschewski T, et al. Hierarchical associations of alcohol use disorder symptoms in late adolescence with markers during early adolescence. Addict Behav. 2020;100:106130.
   PubMed Web of Science ®Google Scholar
• Schivinski B. Eliciting brand-related social media engagement: a conditional inference tree framework. J Bus Res. 2019.
   Google Scholar
• Salum JH, Kitali AE, Bwire H, et al. Severity of motorcycle crashes in Dar es Salaam, Tanzania. Traffic Inj Prev. 2019;20(2):189–195.
   PubMed Web of Science ®Google Scholar
• Klop J, Khattak AJ. Factors influencing bicycle crash severity on two-lane undivided roadways in North Carolina. Transp Res Rec. 1999;1674(1):78–1109.
   Google Scholar