Improving strategic policies for pedestrian safety enhancement using classification tree modeling

Transportation Research Part A: Policy and Practice

Volumn 85, Issue , 2016, Pages 53-64

  Jung, Soyoung ^a   Qin, Xiao ^b   Oh, Cheol ^a  

^a Hanyang University   (South Korea)

^b University of Wisconsin Milwaukee   (United States)

Author keywords

Classification tree; Contributing factors; Injury severities; Pedestrian safety enhancement; Strategic policies

Indexed keywords

ACCIDENTS; DATA MINING; MOTOR TRANSPORTATION; ROADS AND STREETS;

CLASSIFICATION TREES; CONTRIBUTING FACTOR; INJURY SEVERITY; SAFETY ENHANCEMENT; STRATEGIC POLICY;

PEDESTRIAN SAFETY;

ALGORITHM; DATA MINING; INJURY; PEDESTRIAN; TRANSPORTATION POLICY; TRANSPORTATION SAFETY;

KOREA;

EID: 84955246389     PISSN: 09658564     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.tra.2016.01.002     Document Type: Article

References (39)

1. Allwein E., Schapire R., Singer Y. Reducing multiclass to binary: a unifying approach for margin classifiers. J. Mach. Learn. Res. 2001, 1:113-141.
2. Aziz H., Ukkusuri S., Hasan S. Exploring the determinants of pedestrian-vehicle crash severity in New York city. Accid. Anal. Prev. 2013, 50:1298-1309.
3. Breiman L., Friedman J., Olshen R., Stone C. Classification and Regression Trees 1984, Wardworth Inc., Belmont (CA).
4. Chambers J.M., Hastie T.J. Tree-based models. Statistical Model in S 1993, Chapman & Hall, New York, (Chapter 9).
5. Chang L., Chien J. Analysis of driver injury severity in truck-involved accidents using a non-parametric classification tree model. Saf. Sci. 2013, 51:17-22.
6. Chang L., Wang H. Analysis of traffic injury severity: an application of non-parametric classification tree techniques. Accid. Anal. Prev. 2006, 38:1019-1027.
7. Chen L., Chen C., Ewing R. The relative effectiveness of signal related pedestrian countermeasures at urban intersections-lessons from a New York City case study. Transp. Policy 2014, 32:69-78.
8. Clifton K., Burnier C., Akar G. Severity of injury resulting from pedestrian-vehicle crashes: what can we learn from examining the built environment?. Transp. Res. Part D 2009, 14:425-436.
9. Dai D. Identifying clusters and risk factors of injuries in pedestrian-vehicle crashes in a GIS environment. J. Transp. Geogr. 2012, 24:206-214.
10. Delen D., Sharda R., Bessonov M. Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural network. Accid. Anal. Prev. 2006, 38(3):434-444.
11. Dissanayake S., Lu J. Factors influential in making in making an injury severity difference to older drivers involved in fixed object-passenger car crashes. Accid. Anal. Prev. 2002, 34(5):609-618.
12. Eluru N., Bhat C., Hensher D. A mixed generalized ordered response model for examining pedestrian and bicyclist injury severity level in traffic crashes. Accid. Anal. Prev. 2008, 40:1033-1054.
13. FHWA Safety Program, 2013. Pedestrian & Bicycle Safety <>. http://safety.fhwa.dot.gov/ped_bike/.
14. Fitzpatrick, K., Park, E., 2010. Safety Effectiveness of the HAWK Pedestrian Crossing Treatment, FHWA Report No. FHWA-HRT-10-042.
15. Griswold J., Fishbain B., Washington S., David R., Ragland D.R. Visual assessment of pedestrian crashes. Accid. Anal. Prev. 2011, 43:301-306.
16. Hakkert A.S., Braimaister L. The Use of Exposure and Risk in Road Safety Study 2002, SWOV Institute for Road Safety Research, Leidschendam, The Netherlands, R-2002-12.
17. Han Jiawei, Kamber M., Pei J. Data Mining: Concepts and Techniques 2012, Morgan Kaufmann, Waltham, MA, USA. third ed.
18. Kashani A., Mohaymany A. Analysis of the traffic injury severity on two-lane, two-way rural roads based on classification tree models. Saf. Sci. 2011, 49:1314-1320.
19. Kim J., Ulfarsson G., Shankarc V., Mannering F. A note on modeling pedestrian-injury severity in motor-vehicle crashes with the mixed logit model. Accid. Anal. Prev. 2010, 42:1751-1758.
20. Korea Ministry of Government Legislation, 2013. Korea Road Traffic Act <>. http://www.law.go.kr/lsEfInfoP.do?lsiSeq=140211#0000.
21. Korean Statistics Information Service, 2013. Statistics Korea <>. http://kosis.kr/themes/themes_04%20List.jsp.
22. KoRoad Traffic Authority, 2013. Traffic Accident Analysis System <>. http://taas.koroad.or.kr/Eng/indexMain.jsp.
23. Mohamed M., Saunier N., Miranda-Moreno L., Ukkusuri S. 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.
24. Montella A., Aria M., D'Ambrosio A., Mauriello F. Data-mining techniques for exploratory analysis of pedestrian crashes. Transport. Res. Rec.: J. Transport. Res. Board 2011, (2237):107-116. Transportation Research Board of the National Academies, Washington, D.C.
25. National Highway Traffic Safety Administration, 2013. Fatality Analysis Reporting System Web-Based Encyclopedia <>. http://www-fars.nhtsa.dot.gov/People/PeoplePedestrians.aspx.
26. OECD International Transport Forum (ITF), 2010. A Record Decade for Road Safety Press Release.
27. OECD International Transport Forum (ITF), 2013. Road Safety Annual Report.
28. OECD International Transport Forum (ITF), 2014. Road Safety Annual Report.
29. Persaud B., Lana B., Lyon C., Bhim R. Comparison of empirical Bayes and full Bayes approaches for before-after road safety evaluations. Accid. Anal. Prev. 2010, 42(1):38-43.
30. Richmond S., Rothman L., Buliungd R., Schwartz N., Larsend K., Howard A. Exploring the impact of a dedicated streetcar right-of-way on pedestrian motor vehicle collisions: a quasi experimental design. Accid. Anal. Prev. 2014, 71:222-227.
31. Rifaat S., Tay R., Barros A. Effect of street pattern on the severity of crashes involving vulnerable road users. Accid. Anal. Prev. 2011, 43:276-283.
32. Stewart J. Applications of classification and regression tree methods in roadway safety studies. Transport. Res. Rec.: J. Transport. Res. Board 1996, (1542):1-5. Transportation Research Board of the National Academies, Washington, D.C.
33. Strobl C., Boulesteix A., Zeileis A., Hothorn T. Bias in random forest variable importance measures: illustration, Source and a Solution. BMC Bioinform. 2007, 8(1):20.
34. Sullivan, J. M., Flannagan, M.J., 2001. Characteristics of Pedestrian Risk in Darkness, Transportation Research Institute, The University of Michigan, Report No. UMTRI-2001-33.
35. Tax, D., Duin, R., 2002. Using two-class classifiers for multiclass classification. In: International Conference on Pattern Recognition, pp. 124-127.
36. Thompson L., Rivara F., Ayyagari R., Ebel B. Impact of social and technological distraction on pedestrian crossing behaviour: an observational study. Injury Prevent. 2013, 19:232-237.
37. Wang Y., Kockelman K. A Poisson-lognormal conditional-autoregressive model for multivariate spatial analysis of pedestrian crash counts across neighborhoods. Accid. Anal. Prev. 2013, 60:71-84.
38. Washington S. Iteratively specified tree-based regression: theory and trip generation example. J. Transport. Eng. 2000, 126(6):482-491.
39. Zegeer, C.V., Stewart, J. R., Huang, H.H., Lagerwey, P.A., Feaganes, J., Campbell, B.J., 2005. Safety Effects of Marked versus Unmarked Crosswalks at Uncontrolled Locations: Final Report and Recommended Guidelines, FHWA Report No. FHWA-HRT-04-100.