A freeway safety strategy for advanced proactive traffic management

Journal of Intelligent Transportation Systems: Technology, Planning, and Operations

Volumn 9, Issue 3, 2005, Pages 145-158

  Pande, Anurag ^a   Abdel Aty, Mohamed ^a  

^a UNIVERSITY OF CENTRAL FLORIDA   (United States)

Author keywords

Crash Risk; Logistic Regression; Proactive Freeway Management; Real Time Safety Analysis

Indexed keywords

ACCIDENT PREVENTION; CELLULAR TELEPHONE SYSTEMS; HIGHWAY ACCIDENTS; LAW ENFORCEMENT; MOBILE TELECOMMUNICATION SYSTEMS; REGRESSION ANALYSIS; SAFETY FACTOR;

CRASH-RISK; LOGISTIC REGRESSION; PROACTIVE FREEWAY MANAGEMENT; REAL-TIME SAFETY ANALYSIS;

TRAFFIC CONTROL;

EID: 25444449022     PISSN: 15472450     EISSN: 15472442     Source Type: Journal    
DOI: 10.1080/15472450500183789     Document Type: Review

References (13)

1. Abdel-Aty, M. and Pande, A. (2004). Classification of real-time traffic speed patterns to predict crashes on freeways. Presented at the 83rd Annual Meeting of the Transportation Research Board, Washington D.C.
2. Abdel-Aty, M., Uddin, N., Abdalla, F., Pande, A., and Hsia, L. (2004). Predicting freeway crashes based on loop detector data using matched case-control logistic regression. Transportation Research Record, No. 1897, Transportation Research Board, National Research Council, Washington, D.C., 88-95.
3. Abdel-Aty, M., Pande, A., Hsia, L., and Abdalla, F. (2005a). The potential of loop detector data in improving freeway safety. Forthcoming in the ITE Journal.
4. Abdel-Aty, M., Uddin, N., and Pande, A. (2005b). Split models for predicting multivehicle crashes during high-speed and low-speed operating conditions on freeways. Forthcoming in the Transportation Research Record.
5. Agresti, A. (2002). Categorical data analysis, 2nd Ed. John Wiley and Sons, Inc., New York, NY.
6. Golob, T. and Recker, W. (2001). Relationships among urban freeway accidents, traffic flow, weather, and lighting conditions. California PATH Working Paper UCB-ITS-PWP-2001-19, Institute of Transportation Studies. Berkeley, University of California.
7. Golob, T. and Recker, W. (2004). A method for relating type of crash to traffic flow characteristics on urban freeways. Transportation Research Part A: Policy and Practice, 38, 53-80.
8. Hughes, R. and Council, F. (1999). On establishing relationship(s) between freeway safety and peak period operations: Performance measurement and methodological considerations. Presented at the 78th Annual Meeting of Transportation Research Board, Washington, D.C.
9. Lee, C., Saccomanno, F., and Hellinga, B. (2002). Analysis of crash precursors on instrumented freeways. Transportation Research Record, No. 1784, Transportation Research Board, 1-8. National Research Council, Washington, D.C.
10. Lee, C., Saccomanno, F., and Hellinga, B. (2003). Real-time crash prediction model for the application to crash prevention in freeway traffic. Transportation Research Record, No. 1840, Transportation Research Board. National Research Council, Washington, D.C. 67-78.
11. Madanat, S. and Liu, P. (1995). A prototype system for real-time incident likelihood prediction. IDEA Project Final Report (ITS-2), Transportation Research Board. National Research Council, Washington, D.C.
12. Oh, C., Oh, J., Ritchie, S., and Chang, M. (2001). Real time estimation of freeway accident likelihood. Presented at the 80th Annual Meeting of Transportation Research Board, Washington, D.C.
13. SAS® Institute Inc. (1990). SAS/STAT® User's Guide. Version 6, Fourth Ed. Cary, NC.