Detecting Unsafe Roadways with Spatial Statistics: Point Patterns and Geostatistical Models
Publication: Journal of Transportation Engineering
Volume 136, Issue 5
Abstract
A detection scheme that uses classical and spatial statistics has been developed to identify roadways with the most severe safety needs. It is based on the null hypothesis that all roadways have the same crash risk, that is, all have the same nonfatal and fatal crash rates throughout the entire study region. Fatal and nonfatal crash rates, which are assumed to be randomly distributed as Poisson processes, are modeled with a marked homogeneous Poisson process model. Since the traffic exposures are typically unknown at the crash sites, they are predicted with a geostatistical model. Locations, where the null hypothesis is rejected, are safety treatment candidates. -value risk rankings are used to identify locations with the most severe safety needs. The results from alternative analyses—one using vehicle miles traveled and another using population as measures of traffic exposure—are conducted and compared. The state of New Hampshire is used in a case study. The effects of analyzing areas with small traffic exposures, the so-called “small-area estimation” problem, and the aim to develop a data-driven detection scheme that meets the requirements as an objective decision-making tool are discussed.
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Acknowledgments
This material is based upon work supported by the National Science Foundation under Grant No. NSF0525065. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the writers and do not necessarily reflect the views of the National Science Foundation.
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Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 136 • Issue 5 • May 2010
Pages: 457 - 464
Copyright
© 2010 ASCE.
History
Received: Nov 3, 2008
Accepted: Mar 6, 2009
Published online: Mar 19, 2009
Published in print: May 2010
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