Identification of crash-contributing factors

Transportation Research Record

Volumn , Issue 2386, 2013, Pages 179-188

  Manepalli, U ^a   Bham, Ghulam ^b  

^a AgileAssets Inc   (United States)

^b UNIVERSITY OF ALASKA ANCHORAGE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

SAMPLING;

ARKANSAS; CONTRIBUTING FACTOR; CRASH DATA; CRASH SEVERITY; MORAN I; MULTINOMIAL LOGISTIC REGRESSION; SAMPLE DATA; SAMPLE SETS; SPATIAL AUTOCORRELATIONS; Z VALUE;

REGRESSION ANALYSIS;

EID: 84897127428     PISSN: 03611981     EISSN: None     Source Type: Journal    
DOI: 10.3141/2386-20     Document Type: Article

References (35)

1. iUN Decade of Action for Road Safety, 2011 to 2020. Road Safety Fund and World Health Organization. http://www.decadeofaction.org
2. Traffic Safety Facts: 2005. NHTSA, U. S. Department of Transportation http://www-nrd.nhtsa.dot.gov/Pubs/810623.PDF.
3. iMoDOT's Bolder Five-Year Direction: A Presentation to the Missouri Highways and Transportation Commission. Missouri DOT, Jefferson City, June 2011. http://www.modot.org/bolderfiveyeardirection/documents/FINAL-June8plan.pdf
4. Songchitruksa, P., and X. Zeng. Getis-Ord Spatial Statistics to IdentFy Hot Spots by Using Incident Management Data. In iTransportation Research Record: Journal of the Transportation Research Board, No. 2165, Transportation Research Board of the National AcademEs, Washington, D. C., 2010, pp. 42-51.
5. Depue, L. iNCHRP Synthesis of Highway Practice 322: Safety Management Systems. Transportation Research Board of the National AcademEs, Washington, D. C., 2003.
6. Norden, M., J. Orlansky, and H. Jacobs. Application of Statistical Quality-Control Techniques to Analysis of Highway-Accident Data. In iHighway Research Board Bulletin 117, HRB, National Research Council, Washington, D. C., 1956, pp. 17-31.
7. Hakkert, A. S., and D. Mahalel. Estimating the Number of Accidents at Intersections from a Known Traffic Flow on the Approaches. iAccident Analysis and Prevention, Vol. 10, No. 1, 1978, pp. 69-79.
8. McGuigan, D. R. D. The Use of RelationshPs Between Road Accidents and Traffic Flow in "Black-Spot" IdentFication. iTraffic Engineering and Control, 1981, pp. 448-453.
9. Kim, K., D. Takeyama, and L. Nitz. Moped Safety in Honolulu, Hawaii. Journal of Safety Research, Vol. 26, No. 3, 1995, pp. 177-185.
10. McMahon, P. A. iQuantitative and Qualitative Analysis of the Factors Contributing to Collisions Between Pedestrians and Vehicles Along Roadway Segments. MS thesis. University of North Carolina at Chapel Hill, 1999.
11. Peled, A., B. Haj-Yehia, and A. S. Hakkert. ARC/INFO-Based Geographical Information System for Road Safety Analysis and Improvement. Proc., ESRI International User Conference, Palm Springs, CalF., 1996. http://www.esri.com/ library/userconf/proc96/TO50/PAP005/P5.HTM. Accessed Nov. 2009.
12. Premo, L. S. Local Spatial Autocorrelation Statistics QuantFy Multi-Scale Patterns in Distributional Data: An Example from the Maya Lowlands. Journal of Archaeological ScEnce, Vol. 31, No. 7, 2004, pp. 855-866.
13. Huang, H., H. C. Chin, and M. M. Haque. Empirical Evaluation of Alternative Approaches in IdentFying Crash Hot Spots: Nave Ranking, Empirical Bayes, and Full Bayes Methods. In iTransportation Research Record: Journal of the Transportation Research Board, No. 2103, Transportation Research Board of the National AcademEs, Washington, D. C., 2009, pp. 32-41.
14. Quddus, M. A. Modeling Area-Wide Count Outcomes with Spatial Correlation and Heterogeneity: An Analysis of London Crash Data. iAccident Analysis and Prevention, Vol. 40, No. 4, 2008, pp. 1486-1497.
15. Miaou, S., J. J. Song, and B. K. Mallick. Roadway Traffic Crash Mapping: A Space-Time Modeling Approach. Journal of Transportation and Statistics, Vol. 6, No. 1, 2003, pp. 33-57.
16. Huang, H., M. A. Abdel-Aty, and A. L. Darwiche. County-Level Crash Risk Analysis in Florida: Bayesian Spatial Modeling. In iTransportation Research Record: Journal of the Transportation Research Board, No. 2148, Transportation Research Board of the National AcademEs, Washington, D. C., 2010, pp. 27-37.
17. Siddiqui, C., M. Abdel-Aty, and K. Choi. Macroscopic Spatial Analysis of Pedestrian and Bicycle Crashes. iAccident Analysis and Prevention, Vol. 45, 2012, pp. 382-391.
18. Li, L., L. Zhu, and D. Z. Sui. A GIS-Based Bayesian Approach for Analyzing Spatial-Temporal Patterns of Intra-City Motor Vehicle Crashes. Journal of Transport Geography, Vol. 15, No. 4, 2007, pp. 274-285.
19. Tobler, W. A Computer MovE Simulating Urban Growth in the Detroit Region. Economic Geography, Vol. 46, No. 9, 1970, pp. 234-240.
20. Guerts, E., G. Wets, T. BrJis, and K. Vanhoof. IdentFication and Ranking of Black Spots. In iTransportation Research Record: Journal of the Transportation Research Board, No. 1897, Transportation Research Board of the National AcademEs, Washington, D. C., 2004, pp. 34-42.
21. Hauer, E., B. K. Allery, J. Kononov, and M. S. Grffith. How Best to Rank Sites with Promise. In iTransportation Research Record: Journal of the Transportation Research Board, No. 1897, Transportation Research Board of the National AcademEs, Washington, D. C., 2004, pp. 48-54.
22. Ye, F., and D. Lord. Investigation of Effects of Underreporting Crash Data on Three Commonly Used Traffic Crash Severity Models: Multinomial Logit, Ordered Probit, and Mixed Logit. In iTransportation Research Record: Journal of the Transportation Research Board, No. 2241, Transportation Research Board of the National AcademEs, Washington, D. C., 2011, pp. 51-58.
23. Mitra, S. Spatial Autocorrelation and Bayesian Spatial Statistical Method for Analyzing Intersections Prone to Injury Crashes. In iTransportation Research Record: Journal of the Transportation Research Board, No. 2136, Transportation Research Board of the National AcademEs, Washington, D. C., 2009, pp. 92-100.
24. Moran, P. A. Notes on Continuous Stochastic Phenomena. iBiometrika, No. 37, 1950, pp. 17-33.
25. Getis, A., and J. K. Ord. The Analysis of Spatial Association by Use of Distance Statistics. iGeographic Analysis, Vol. 24, No. 3, 1992, pp. 189-206.
26. Ord, J. K., and A. Getis. Local Spatial Autocorrelation Statistics: Distributional Issues and an Application. iGeographic Analysis, Vol. 27, No. 4, 1995, pp. 286-306.
27. KrygEr, J. B. iGeog 353 Lecture Outline: Map Generalization and ClassFication (geography lecture notes). http://go.owu.edu/~jbkrygE/krygEr-html/ geog-353/geog-353-lo/geog-353-lo07.html
28. ArcGIS 9.2 Desktop Help Online Manual. http://webhelp.esri.com/ arcgisdesktop/9.2/index.cfm?TopicName=Standard-classFication-schemes
29. Lewis, D. J. Jenks' Natural Breaks Algorithm in Python. Department of Geography, University College London, June 7, 2010. http://danEljlewis.org/2010/ 06/07/jenks-natural-breaks-algorithm-in-python. Accessed June 25, 2010.
30. Manepalli, U. R. R., and G. H. Bham. An Evaluation of Crash Frequency, Crash Severity, and Composite Rank Methods for Hotspot IdentFication. Presented at 91st Annual Meeting of the Transportation Research Board, Washington, D. C., 2012.
31. Highway Safety Manual, 1st ed. AASHTO, Washington, D. C., 2010, Vol. 1, Section 4-66, Table 4A-1.
32. Bham, G. H., B. S. Javvadi, and U. R. R. Manepalli. Multinomial Logistic Regression Model for Single-Vehicle and Multivehicle Collisions on Urban U. S. Highways in Arkansas. Journal of Transportation Engineering, ASCE, Vol. 138, No. 6, 2012, pp. 786-797.
33. SAS OnlineDoc 9.1.2 SAS Institute, Cary, N. C. http://support.sas.com/ onlinedoc/912/docMainpage.jsp.
34. Bham, G. H., and U. R. R. Manepalli. iIdentFication and Analysis of High Crash Segments on Interstate, US, and State Highway Systems of Arkansas. Report MBTC 2099/3006. Mack-Blackwell Rural Transportation Center; Missouri University of ScEnce and Technology, Rolla, 2009.
35. Manepalli, U. R. R. iA Statewide Analysis of Highway Safety in Arkansas. MS thesis. Missouri University of ScEnce and Technology, Rolla, 2011.