Multiple applications of multivariate adaptive regression splines technique to predict rear-end crashes at unsignalized intersections

Transportation Research Record

Volumn , Issue 2165, 2010, Pages 33-41

  Haleem, Kirolos ^a   Abdel Aty, Mohamed ^a   Santos, Joseph ^b  

^a University of Central Florida   (United States)

^b FLORIDA DEPARTMENT OF TRANSPORTATION   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; DECISION TREES; FORECASTING; HIGHWAY ACCIDENTS; HIGHWAY ENGINEERING; LEARNING SYSTEMS; MOTOR TRANSPORTATION; SPLINES;

CRASH FREQUENCY; CRASH PREDICTION; HIGHWAY SAFETY; MULTIPLE APPLICATIONS; MULTIVARIATE ADAPTIVE REGRESSION SPLINES; MULTIVARIATE ADAPTIVE REGRESSION SPLINES MODELS; PREDICTION MODELLING; RANDOM FORESTS; REAR-END CRASHES; UNSIGNALIZED INTERSECTIONS;

LAND USE;

EID: 78651303001     PISSN: 03611981     EISSN: None     Source Type: Journal    
DOI: 10.3141/2165-04     Document Type: Article

References (35)

1. Florida Strategic Highway Safety Plan, Florida Department of Transportation, 2006.
2. Abbess, C., D. Jarett, and C. Wright. Accidents at Blackspots: Estimating the Effectiveness of Remedial Treatment, with Special Reference to the "Regression-to-Mean" Effect. Traffic Engineering and Control, Vol. 22, No. 10, 1981, pp. 535-542.
3. Kulmala, R. Safety at Rural Three- and Four-Arm Junctions: Development and Applications of Accident Prediction Models. VTT Publications 233. Technical Research Center of Finland, Espoo, 1995.
4. Lord, D. The Prediction of Accidents on Digital Networks: Characteristics and Issues Related to the Application of Accident Prediction Models. PhD dissertation. Department of Civil Engineering, University of Toronto, Ontario, Canada, 2000.
5. Miaou, S.-P., and D. Lord. Modeling Traffic Crash-Flow Relationships for Intersections: Dispersion Parameter, Functional Form, and Bayes Versus Empirical Bayes Methods. In Transportation Research Record: Journal of the Transportation Research Board, No. 1840, Transportation Research Board of the National Academies, Washington, D.C., 2003, pp. 31-40.
6. Xie, Y., D. Lord, and Y. Zhang. Predicting Motor Vehicle Collisions Using Bayesian Neural Network Models: An Empirical Analysis. Accident Analysis & Prevention, Vol. 39, No. 5, 2007, pp. 922-933.
7. Vogt, A. Crash Models for Rural Intersections: Four-Lane by Two- Lane Stop-Controlled and Two-Lane by Two-Lane Signalized. FHWARD- 99-128. Final Report. FHWA, U.S. Department of Transportation, 1999.
8. Poch, M., and F. Mannering. Negative Binomial Analysis of Intersection Accident Frequencies. Journal of Transportation Engineering, Vol. 122, No. 2, 1996.
9. Mussone, L., A. Ferrari, and M. Oneta. An Analysis of Urban Collisions Using an Artificial Intelligence Model. Accident Analysis & Prevention, Vol. 31, No. 6, 1999, pp. 705-718.
10. Abdelwahab, H. T., and M. A. Abdel-Aty. Artificial Neural Networks and Logit Models for Traffic Safety Analysis of Toll Plazas. In Transportation Research Record: Journal of the Transportation Research Board, No. 1784, Transportation Research Board of the National Academies, Washington, D.C., 2002, pp. 115-125.
11. Riviere, C., P. Lauret, J. Ramsamy, and Y. Page. A Bayesian Neural Network Approach to Estimating the Energy Equivalent Speed. Accident Analysis and Prevention, Vol. 38, No. 2, 2006, pp. 248-259.
12. Li, X., D. Lord, Y. Zhang, and Y. Xie. Predicting Motor Vehicle Crashes Using Support Vector Machine Models. Accident Analysis and Prevention, Vol. 40, No. 4, 2008, pp. 1611-1618.
13. Friedman, J. Multivariate Adaptive Regression Splines. Annals of Statistics, Vol. 19, 1991.
14. Put, R., Q. Xu, D. Massart, and Y. Heyden. Multivariate Adaptive Regression Splines (MARS) in Chromatographic Quantitative Structure-Retention Relationship Studies. Journal of Chromatography A, 1055, 2004, pp. 11-19.
15. Attoh-Okine, N., S. Mensah, and M. Nawaiseh. A New Technique for Using Multivariate Adaptive Regression Splines (MARS) in Pavement Roughness Prediction. Proc., Institution of Civil Engineers, Vol. 156, No. 1, Feb. 2003, pp. 51-55.
16. Abraham, A., D. Steinberg, and N. Philip. Rainfall Forecasting Using Soft Computing Models and Multivariate Adaptive Regression Splines. IEEE SMC Transactions: Special Issue on Fusion of Soft Computing and Hard Computing in Industrial Applications, Feb. 2001.
17. Xiong, R., and J. Meullenet. Application of Multivariate Adaptive Regression Splines (MARS) to the Preference Mapping of Cheese Sticks. Journal of Food Science, Vol. 69, 2004.
18. Sekulic, S., and B. Kowalski. MARS-A Tutorial. Journal of Chemometrics, Vol. 6, No. 4, 199-216, July-Aug. 1992.
19. Breiman, L. Random Forests. Machine Learning, Vol. 45, No. 1, Oct. 2001, pp. 5-32.
20. Harb, R., X. Yan, E. Radwan, and X. Su. Exploring Precrash Maneuvers Using Classification Trees and Random Forests. Accident Analysis and Prevention, Vol. 41, No. 1, Jan. 2009, pp. 98-107.
21. Kuhn, S., B. Egert, S. Neumann, and C. Steinbeck. Building Blocks for Automated Elucidation of Metabolites: Machine Learning Methods for NMR Prediction. BMC Bioinformatics, Vol. 9, Sept. 2008, pp. 5-32.
22. R Software. http://www.r-project.org/. Accessed April 27, 2009.
23. Lord, D., and M. Mahlawat. Examining Application of Aggregated and Disaggregated Poisson-Gamma Models Subjected to Low Sample Mean Bias. In Transportation Research Record: Journal of the Transportation Research Board, No. 2136, Transportation Research Board of the National Academies, Washington, D.C., 2009, pp. 1-10.
24. Jonsson, T., C. Lyon, J. N. Ivan, S. P. Washington, I. van Schalkwyk, and D. Lord. Differences in the Performance of Safety Performance Functions Estimated for Total Crash Count and for Crash Count by Crash Type. In Transportation Research Record: Journal of the Transportation Research Board, No. 2102, Transportation Research Board of the National Academies, Washington, D.C., 2009, pp. 115-123.
25. Summersgill, I., and J. Kennedy. Accidents at Three-Arm Priority Functions on Urban Single Carriageway Roads. TRL Report 184. Transport Research Laboratory, Crowthorne, United Kingdom, 1996, p. 74.
26. Layfield, R. Accidents at Urban Priority Crossroads and Staggered Junctions. TRL Report 185. Transport Research Laboratory, Crowthorne, United Kingdom, 1996, p. 120.
27. Crash Analysis Reporting System. http://tlhost01.dot.state.fl.us/ bluezone/ FDOT-Session/default.htm. Accessed Jan. 20, 2008.
28. Google Earth. http://earth.google.com/. Accessed Jan. 20, 2008.
29. Video Log Viewer Application. http://webapp01.dot.state.fl.us/videolog/. Accessed Feb. 10, 2008.
30. Roadway Characteristic Inventory. http://webapp01.dot.state.fl.us/Login/ default.asp. Accessed Feb. 10, 2008.
31. SAS Institute Inc. Version 9 of the SAS System for Windows. Cary, N.C., 2002.
32. Zuur, A., E. Ieno, and G. Smith. Analyzing Ecological Data. Statistics for Biology and Health, Springer, New York, 2007.
33. Wang, X., and M. Abdel-Aty. Temporal and Spatial Analyses of Rear-End Crashes at Signalized Intersections. Accident Analysis and Prevention, Vol. 38, 2006, pp. 1137-1150.
34. Phillips, S. Empirical Collision Model for Four-Lane Median Divided and Five-Lane with TWLTL Segments. MS thesis. North Carolina State University, Raleigh, 2004.
35. Pham, M., O. de Mouzon, E. Chung, and N. El Faouzi. Sensitivity of Road Safety Indicators in Normal and Crash Cases. 10th International Conference on Application of Advanced Technologies in Transportation, Athens, Greece, May 27-31, 2008.