PUBLICATION

Research Report 11-15

Research ReportProactive Assessment of Accident Risk to Improve Safety on a System of Freeways (PDF 2.3MB)

Principal Investigator: Anurag Pande, Ph.D.

ABSTRACT

This report describes the development and evaluation of real-time crash risk-assessment models for four freeway corridors: U.S. Route 101 NB (northbound) and SB (southbound) and Interstate 880 NB and SB. Crash data for these freeway segments for the 16-month period from January 2010 through April 2011 are used to link historical crash occurrences with real-time traffic patterns observed through loop-detector data.

'The crash risk-assessment models are based on a binary classification approach (crash and non-crash outcomes), with traffic parameters measured at surrounding vehicle detection station (VDS) locations as the independent variables. The analysis techniques used in this study are logistic regression and classification trees.

Prior to developing the models, some data-related issues such as data cleaning and aggregation were addressed. The modeling efforts revealed that the turbulence resulting from speed variation is significantly associated with crash risk on the U.S. 101 NB corridor. The models estimated with data from U.S. 101 NB were evaluated on the basis of their classification performance, not only on U.S. 101 NB, but also on the other three freeway segments for transferability assessment. It was found that the predictive model derived from one freeway can be readily applied to other freeways, although the classification performance decreases. The models that transfer best to other roadways were determined to be those that use the least number of VDSs–that is, those that use one upstream or downstream station rather than two or three.\

The classification accuracy of the models is discussed in terms of how the models can be used for real-time crash risk assessment. The models can be applied to developing and testing variable speed limits (VSLs) and ramp-metering strategies that proactively attempt to reduce crash risk.

ABOUT THE AUTHORS

ANURAG PANDE, PH.D.

Anurag Pande, Ph.D., is an Assistant Professor of Civil Engineering at California Polytechnic State University, San Luis Obispo. In addition, he is the coordinator for the dual-degree program in civil engineering and city and regional planning. He is a Research Associate of the Mineta Transportation Institute.

CORNELIUS NUWORSOO, PH.D.

Cornelius Nuworsoo, Ph.D., is an Associate Professor of Transportation Planning at California Polytechnic State University, San Luis Obispo. He also serves as graduate programs coordinator in the Department of City and Regional Planning. He is a Research Associate of the Mineta Transportation Institute.

CAMERON SHEW

Cameron Shew is a Master's degree candidate in Civil and Environmental Engineering at California Polytechnic State University, San Luis Obispo. He is a certified engineer-in-training (EIT) and has interned with Fehr & Peers Transportation Consultants (June 2011–September 2011) and Kittelson & Associates, Inc. (June 2010–September 2010). His interests include transportation design and traffic simulation and safety.

TECHNICAL

Authors: Anurag Pande, Ph.D., Cornelius Nuworsoo, Ph.D. and Cameron Shew
Published: May 2012
Keywords: Real-time crash risk, Data mining, Classification tree, Proactive traffic management, Loop detector data