Planning Level Regression Models for Prediction of Crashes on Interchange and Noninterchange Segments of Urban Freeways
Publication: Journal of Transportation Engineering
Volume 134, Issue 3
Abstract
The need for safety assessment tools for long-range transportation planning at statewide and metropolitan levels received serious recognition when the Transportation Equity Act for Twenty First Century established a requirement related to safety considerations in the planning process of metropolitan planning organizations (MPO) and state Departments of Transportation. The latest legislation titled “Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users” also has confirmed this requirement. However, most MPOs do not currently assess the safety consequences of alternative transportation systems, and one of the reasons is the lack of suitable methodology. The goal of the research on which this paper is based was to develop practical tools for assessing safety consequences of freeways in the context of long-range urban transportation plans. Data for crashes, identified by freeway segments, were obtained from the North Carolina Department of Transportation and Tennessee Department of Transportation. The researchers used the negative-binomial regression modeling approach to develop separate models to predict number of crashes for different levels of crash severity for noninterchange segments, and interchange segments, respectively. A major consideration for the selection of independent variables of the models was planners’ ability to forecast future values of the model variables for alternative highway networks. The paper presents crash prediction models that MPO planners can use to evaluate the safety impact of alternative freeway networks when comparing their costs and benefits in the long-range planning context. These models are not meant to be used for analyzing detailed design features of urban freeways during project development stage, which usually is the responsibility of state Department of Transportation.
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Acknowledgments
The work presented in this paper was sponsored by the Southeastern Transportation Center with funding from the U.S. Department of Transportation. The writers are grateful to Ms. DeAnna Flinchum of STC for her support. The writers are also grateful to the North Carolina and Tennessee DOTs for providing data for this research. At the NCDOT, Tony Ku and Kevin Lacy were tireless in their efforts to supply the data we requested. The TDOT data were processed by Vickie Schultze of the University of Tennessee, and her help was invaluable. The data and conclusions presented in this paper are those of the writers alone and do not represent the viewpoints of any other entity. The writers are solely responsible for the accuracy of the data and conclusions presented in the paper.
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Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 134 • Issue 3 • March 2008
Pages: 111 - 117
Copyright
© 2008 ASCE.
History
Received: Nov 9, 2005
Accepted: Aug 27, 2007
Published online: Mar 1, 2008
Published in print: Mar 2008
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