Analyzing injury severity factors at highway railway grade crossing accidents involving vulnerable road users: A comparative study

References

• Abdel-Aty M. Analysis of driver injury severity levels at multiple locations using ordered probit models. J Safety Res. 2003;34:597–603.
   PubMed Web of Science ®Google Scholar
• Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in large databases. Paper presented at: ACM SIGMOD; 1993; Washington, DC.
   Google Scholar
• Ayramo S, Pirtala P, Kauttonen J, Naveed K, Karkkainen T. Mining road traffic accidents. Reports of the Department of Mathematical Information Technology Series C. Software and Computational Engineering. 2009;2.
   Google Scholar
• Bajracharya SH. Intersection accident severity analysis using ordered probit model. Proceedings of the Eastern Asia Society for Transportation Studies. 2013;9:379.
   Google Scholar
• Beshah T, Hill SH. Mining Road Traffic Accident Data to Improve Safety: Role of Road-Related Factors on Accident Severity in Ethiopia. Addis Ababa, Ethiopia: Addis Ababa University; 2007.
   Google Scholar
• Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and Regression Trees. Belmont, CA: Wadsworth International Group; 1984.
   Google Scholar
• Brin S, Motwani R. Beyond market baskets: generalizing association rules to dependence rules. Data Min Knowl Discov. 1998;2:39–68.
   Web of Science ®Google Scholar
• Cameron AC, Windmeijer FAG. An R-squared measure of goodness of fit for some common nonlinear regression models. Journal of Econometrics. 1997;77(2):329--342.
   Web of Science ®Google Scholar
• Cina SJ, Koelpin JL, Nichols CA, Conradi SE. A decade of train–pedestrian fatalities: the Charleston experience. J Forensic Sci. 1994;39:668–673.
   PubMed Web of Science ®Google Scholar
• Davis GG, Bruce AC, Brissie RM. A 15-year review of railway related deaths in Jefferson County, Alabama. Am J Forensic Med Pathol. 1997;18:363–368.
   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;1635:63–71.
   Web of Science ®Google Scholar
• Eluru N, Bagheri M, Miranda-Moreno L, Fu L. A latent class modelling approach for identifying vehicle driver injury severity factors at highway–railway crossings. Collision Analysis and Prevention. 2012;47:119–127.
   Google Scholar
• Evans AW. Accidental fatalities in transport. J R Stat Soc Ser A Stat Soc. 2003;166:253–260.
   Web of Science ®Google Scholar
• Fan W, Kane M, Haile E. Predicting the severity of pedestrian crashes on highway–rail grade crossings. Advances in Transportation Studies an International Journal Section A. 2015;36:63--74.
   Google Scholar
• Federal Railroad Administration. A Compilation of Pedestrian Safety Devices in Use at Grade Crossings. Federal Railroad Administration, Office of Safety; 2008.
   Google Scholar
• Federal Railroad Administration, Office of Safety Analysis Website. Available at: http://www.fra.dot.gov
   Google Scholar
• Geurts KG, Wets T, Vanhoof BK. Profiling High Frequency Accident Locations Using Association Rules. Limburg University Centrum; 2002.
   Google Scholar
• Han J, Kamber M. Data Mining: Concepts and Techniques. Elsevier Inc.; 2006.
   Google Scholar
• Hao W, Daniel J. Motor Vehicle Driver Injury Severity Study at Highway–Rail Grade Crossings in the United States. Transportation Research Board; 2013.
   Google Scholar
• Huntley-Fenner G. Warnings and behavior: a study of pedestrian behavior at grade crossings. Paper presented at: 2008 Rail Corridor Safety Conference: A Railroad Runs through It: Working Together for Mobility & Safety; 2008.
   Google Scholar
• Illinois Commerce Commission. Proposed Grade Crossing Protection Fund Projects for Local Roads and Streets. Crossing Safety Improvement Program; 2009.
   Google Scholar
• Khattak A, Luo ZH. Pedestrian and Bicyclist Violations at Highway–Rail Grade Crossings. Transportation Research Board; 2011.
   Google Scholar
• Kockelman KM, Kweon YJ. Driver injury severity: an application of ordered probit models. Accid Anal Prev. 2002;34:313–321.
   PubMed Web of Science ®Google Scholar
• Kwigizile V, Sando T, Chimba D. Inconsistencies of ordered and unordered probability models for pedestrian injury severity. Transp Res Rec. 2011;2264:110–118.
   Web of Science ®Google Scholar
• Lee C, Abdel-Aty M. Comprehensive analysis of vehicle–pedestrian crashes at intersections in Florida. Accid Anal Prev. 2005;37:775–786.
   PubMed Web of Science ®Google Scholar
• Lemp JD, Kockelman KM, Unnikrishnan A. Analysis of large truck crash severity using heteroskedastic ordered probit models. Accid Anal Prev. 2011;43(1):370--380.
   Google Scholar
• Lerer LB, Matzopoulos RG. Fatal railway injuries in Cape Town, South Africa. Am J Forensic Med Pathol. 1997;18:144–147.
   PubMed Web of Science ®Google Scholar
• Lobb B, Harre N, Suddendorf TH. An evaluation of a suburban railway pedestrian crossing safety programme. Accid Anal Prev. 2001;33:157–165.
   PubMed Web of Science ®Google Scholar
• McCollister GM, Pflaum CC. A model to predict the probability of highway rail crossing accidents. Journal of Rail and Rapid Transit. 2007;221:321–329.
   Web of Science ®Google Scholar
• Metaxatos P, Sriraj PS. Advancing pedestrian safety at rail grade crossings. Transp Res Rec. 2012;1(4):238--248.
   Google Scholar
• Mirabadi A, Sharifian SH. Application of association rules in Iranian railways (RAI) accident data analysis. Saf Sci. 2010;48:1427–1435.
   Web of Science ®Google Scholar
• Mohamed MG, Saunier N, Miranda-Moreno LF, Ukkusuri SV. A clustering regression approach: a comprehensive injury severity analysis of pedestrian–vehicle crashes in New York, US and Montreal, Canada. Saf Sci. 2013;54:27–37.
   Web of Science ®Google Scholar
• Montella A, Aria M, D'Ambrosio A, Mauriello F. Data-mining techniques for exploratory analysis of pedestrian crashes. Transp Res Rec. 2011;2237:107–116.
   Web of Science ®Google Scholar
• Nixon J, Corcoran A, Fielding L, Eastgate J. Fatal and nonfatal accidents on the railways—a study of injuries to individuals, with particular reference to children and to nonfatal trauma. Accid Anal Prev. 1985;17:217–222.
   PubMed Web of Science ®Google Scholar
• Pai CW, Saleh W. An Analysis of motorcyclist injury severity under various traffic control measures at three-legged junctions in the UK. Saf Sci. 2007;45:832–847.
   Web of Science ®Google Scholar
• Pakgohar R, Tabrizi S, Khalili M, Esmaeili A. The role of human factor in incidence and severity of road crashes based on the CART and LR regression: a data mining approach. Procedia Comput Sci. 2011;3:764–769.
   Google Scholar
• Park YJ, Saccomanno FF. Evaluating factors affecting safety at highway–railway grade crossings. Transp Res Rec. 2005;1918:1–9.
   Web of Science ®Google Scholar
• Pelletier A. Deaths among railroad trespassers: the role of alcohol in fatal injuries. J Am Med Assoc. 1997;277:1064–1067.
   Web of Science ®Google Scholar
• Quddus M, Quddus A, Noland RB, Chin HC. An analysis of motorcycle injury and vehicle damage severity using ordered probit models. J Safety Res. 2002;33:445–462.
   PubMed Web of Science ®Google Scholar
• Savage I. Analysis of data on pedestrian/rail safety in Illinois 2005–2009. Paper presented at: Northwestern University Transportation Center Pedestrian Rail Safety Symposium; 2010.
   Google Scholar
• Shankar V, Mannering F. An exploratory multinomial logit analysis of single-vehicle motorcycle accident severity. J Safety Res. 1996;27(3):183–194.
   Web of Science ®Google Scholar
• Shanthi S, Ramani RG. Classification of vehicle collision patterns in road accidents using data mining algorithms. Int J Comput Appl. 2001;35(12):975.
   Google Scholar
• Silla A, Luoma J. Effect of three countermeasures against the illegal crossing of railway tracks. Accid Anal Prev. 2011;43:1089–1094.
   PubMed Web of Science ®Google Scholar
• Sohn S, Hyungwon S. Pattern recognition for a road traffic accident severity in Korea. Ergonomics. 2001;44:101–117.
   Web of Science ®Google Scholar
• Stefanova T. Pedestrian safety at level crossing—how the issue should be addressed. Paper presented at: 10th World Congress of Railway Research; 2013.
   Google Scholar
• Stewart JR. Applications of classification and regression tree methods in roadway safety studies. Transp Res Rec. 1996;1542:1--5.
   Google Scholar
• Xie Y, Zhang Y, Liang F. Crash injury severity analysis using Bayesian ordered probit models. J Transp Eng. 2009;135:18–25.
   Web of Science ®Google Scholar
• Yamamoto T, Shankar VN. Bivariate Ordered-Response Probit Model of Driver's and Passenger's Injury Severities in Collision with Fixed Objects. Transportation Research Board; 2002.
   Google Scholar
• Yan X, Richards SH. Using Hierarchical Tree–Based Regression Model to Predict Train Vehicle Crashes at Passive Highway–Rail Grade Crossings. Center for Transportation Research; 2009.
   Google Scholar
• Yan XD. Using hierarchical tree–based regression model to predict train–vehicle crashes at passive highway–rail grade crossings. Accid Anal Prev. 2010;42:64–74.
   PubMed Web of Science ®Google Scholar
• Zajac SS, Ivan JN. Factors influencing injury severity of motor vehicle-crossing pedestrian crashes in rural Connecticut. Accid Anal Prev. 2003;35:369–379.
   PubMed Web of Science ®Google Scholar
• Zhang Y, Li ZB, Liu P, Zha LT. Exploring contributing factors to crash injury severity at freeway diverge areas using ordered probit model. Procedia Eng. 2011;21:178–185.
   Google Scholar