Final report of ITS Center project: Identification of traffic patterns leading to crashes

UVA Center for Transportation Studies

A Research Project Report

For the Center for ITS Implementation Research

A U.S. DOT University Transportation Center

FREEWAY CRASH PREDICTIONS BASED ON REAL-TIME PATTERN CHANGES IN TRAFFIC FLOW CHARACTERISTICS

Principal Investigators:

Dr. Nicholas Garber
Lili Luo

Center for Transportation Studies

University of Virginia

Thornton Hall

351 McCormick Road, P.O. Box 400742

Charlottesville, VA 22904-4742

804.924.6362

January 2006
Research Report No. UVACTS-15-0-101

Disclaimer

The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. This document is disseminated under the sponsorship of the Department of Transportation, University Transportation Centers Program, in the interest of information exchange. The U.S. Government assumes no liability for the contents or use thereof.

Freeway Crash Predictions Based on Real-Time Pattern Changes in Traffic Flow Characteristics

By: Lili Luo

Dr. Nicholas J. Garber

A Research Project Report for the Intelligent Transportation Systems Implementation Center (ITS)

A U.S. DOT University Transportation Center

Dr. Nicholas J. Garber

Department of Civil Engineering

Email: njg@virginia.edu

Center for Transportation Studies at the University of Virginia produces outstanding transportation professionals, innovative research results and provides important public service. The Center for Transportation Studies is committed to academic excellence, multi-disciplinary research and to developing state-of-the-art facilities. Through a partnership with the Virginia Department of Transportation’s (VDOT) Research Council (VTRC), CTS faculty hold joint appointments, VTRC research scientists teach specialized courses, and graduate student work is supported through a Graduate Research Assistantship Program. CTS receives substantial financial support from two federal University Transportation Center Grants: the Mid-Atlantic Universities Transportation Center (MAUTC), and through the National ITS Implementation Research Center (ITS Center). Other related research activities of the faculty include funding through FHWA, NSF, US Department of Transportation, VDOT, other governmental agencies and private companies.

Disclaimer: The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. This document is disseminated under the sponsorship of the Department of Transportation, University Transportation Centers Program, in the interest of information exchange. The U.S. Government assumes no liability for the contents or use thereof.

1. Report No. UVACTS-15-0-101	2. Government Accession No.		3. Recipient’s Catalog No.
4. Title and Subtitle			5. Report Date
Freeway Crash Predictions Based on Real-Time Pattern Changes in Traffic Flow Characteristics			January 20, 2006
			6. Performing Organization Code
7. Author(s) Lili Luo Dr. Nicholas J. Garber (Academic Advisor)			8. Performing Organization Report No.
9. Performing Organization and Address			10. Work Unit No. (TRAIS)
Center for Transportation Studies
University of Virginia			11. Contract or Grant No.
PO Box 400742 Charlottesville, VA 22904-7472
12. Sponsoring Agencies' Name and Address			13. Type of Report and Period Covered
Office of University Programs, Research Innovation and Technology Administration US Department of Transportation 400 Seventh Street, SW Washington DC 20590-0001			Final Report
			14. Sponsoring Agency Code
15. Supplementary Notes
16. Abstract
In recent years, attempts were made to develop a crash prediction model based on real-time detector data. Since studies in this field are primarily theoretical, improvements can be made in various aspects. It is expected that the final product of this study will be a program that integrates with the Advance Traffic Management System so that operators of Smart Travel Centers can take action to prevent or at least reduce the chances of crash occurrence. At this first stage, efforts were made to identify the crash leading patterns and the factors describing the patterns. Crashes that occurred on interstate highway basic segments between July 1, 2003 and June 30, 2004 from Northern Virginia were obtained from police crash reports. The associated traffic conditions as well as the normal non-crash conditions defined by the traffic parameters were collected from Smart Travel Lab. By applying three different pattern recognition techniques - the K-means clustering method; Naïve-Bayes method; and Discriminant Analysis - it was found that the overall classification error rate remained at about 50% and was unable to identify the crash leading patterns.
17 Key Words		18. Distribution Statement