Hit-and-run crashes: Use of rough set analysis with logistic regression to capture critical attributes and determinants

Transportation Research Record

Volumn , Issue 2083, 2008, Pages 114-121

  Kim, Karl ^a   Pant, Pradip ^a   Yamashita, Eric Y ^a  

^a UNIVERSITY OF HAWAII   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCIDENTS; REGRESSION ANALYSIS;

CRITICAL ATTRIBUTES; CRITICAL DETERMINANT; HORIZONTAL ALIGNMENT; KEY DETERMINANTS; KEY FACTORS; LOGISTIC REGRESSION MODELING; LOGISTICS REGRESSIONS; MATHEMATICAL TOOLS; ROUGH SET ANALYSIS;

ROUGH SET THEORY;

EID: 63849095121     PISSN: 03611981     EISSN: None     Source Type: Journal    
DOI: 10.3141/2083-13     Document Type: Article

References (17)

1. Solnick, S., and D. Hemenway. The Hit-and-Run in Fatal Pedestrian Accidents: Victims, Circumstances, and Drivers. Accident Analysis and Prevention, Vol. 27, No. 5, 1995, pp. 643-649.
2. Fischer, M., R. Barkley, L. Smallish, and K. Fletcher. Hyperactive Children as Young Adults: Driving Abilities, Safe Driving Behavior, and Adverse Driving Outcomes. Accident Analysis and Prevention, Vol. 39, 2007, pp. 94-105.
3. Pawlak, Z. Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishing, Norwell, Mass., 1992.
4. Kim, K., L. Nitz, J. Richardson, and L. Li. Personal and Behavioral Predictors of Automobile Crash and Injury Severity. Accident Analysis and Prevention, Vol. 27, No. 4, 1995, pp. 469-481.
5. Kim, K., L. Li, J. Richardson, and L. Nitz. Drivers at Fault: Influences of Age, Sex, and Vehicle Type. Journal of Safety Research, Vol. 29, No. 3, 1998, pp. 171-179.
6. Kim, K., and E. Yamashita. Corresponding Characteristics and Circumstances of Collision-Involved Pedestrians in Hawaii. In Transportation Research Record: Journal of the Transportation Research Board, No. 2073, Transportation Research Board of the National Academies, Washington, D.C., 2008, pp. 18-24.
7. Pawlak, Z. A Primer on Rough Sets: A New Approach to Drawing Conclusions from Data. Cardozo Law Review, Vol. 22, 2002, pp. 1407-1415.
8. Walczak, B., and D. L. Massart. Tutorial Rough Set Theory. Chemometrics and Intelligent Laboratory Systems, Vol. 47, 1988, pp. 1-16.
9. Li, J. Rough Set Based Rule Evaluations and Their Applications. PhD dissertation. University of Waterloo, Waterloo, Ontario, Canada, 2007. hdl.handle.net/10012/2738. Accessed July 20, 2007.
10. Walter, A. I., and W. Scholz. Critical Success Conditions of Collaborative Methods: A Comparative Evaluation of Transport Planning Projects. Transportation, Vol. 34, 2007, pp. 195-212.
11. Chang, J., C. Hung, G. Tzeng, and S. Kang. Using Rough Set Theory to Induce Pavement Maintenance and Rehabilitation Strategy. Presented at Rough Sets and Knowledge Technology: 2nd International Conference (RSKT 2007), Toronto, Ontario, Canada, May 14-16, 2007.
12. Wong, J. T., and Y. S. Chung. Rough Set Approach for Accident Chain Exploration. Accident Analysis and Prevention, Vol. 39, No. 3, 2007, pp. 629-637.
13. Rough Set Exploration System (RSES). Institute of Mathematics, Warsaw University,Warsaw, Poland. logic.mimuw.edu.pl/?rses. Accessed June 20, 2007.
14. Feinberg, S. E. The Analysis of Cross-Classified Categorical Data. MIT Press, Cambridge, Mass., 1977.
15. Agresti, A. Categorical Data Analysis. John Wiley and Sons, Inc., New York, 1990.
16. Kim, K., and E. Yamashita. Using a K-Means Clustering Algorithm to Examine Patterns of Pedestrian Involved Crashes in Honolulu, Hawaii. Journal of Advanced Transportation, Vol. 41. No. 1, 2007, pp. 69-89.
17. Kim, K. The Lie Factor in Traffic Safety. In Transportation Research Record: Journal of the Transportation Research Board, No. 1665, TRB, National Research Council, Washington, D.C., 1999, pp. 141-146.