Applying quantile regression for modeling equivalent property damage only crashes to identify accident blackspots

Washington, Simon, Haque, Md. Mazharul, Oh, Jutaek and Lee, Dongmin (2014) Applying quantile regression for modeling equivalent property damage only crashes to identify accident blackspots. Accident Analysis and Prevention, 66 136-146. doi:10.1016/j.aap.2014.01.007


Author Washington, Simon
Haque, Md. Mazharul
Oh, Jutaek
Lee, Dongmin
Title Applying quantile regression for modeling equivalent property damage only crashes to identify accident blackspots
Journal name Accident Analysis and Prevention   Check publisher's open access policy
ISSN 0001-4575
1879-2057
Publication date 2014-04-01
Sub-type Article (original research)
DOI 10.1016/j.aap.2014.01.007
Open Access Status Not yet assessed
Volume 66
Start page 136
End page 146
Total pages 11
Place of publication Oxford, United Kingdom
Publisher Elsevier
Language eng
Abstract Hot spot identification (HSID) aims to identify potential sites - roadway segments, intersections, crosswalks, interchanges, ramps, etc. - with disproportionately high crash risk relative to similar sites. An inefficient HSID methodology might result in either identifying a safe site as high risk (false positive) or a high risk site as safe (false negative), and consequently lead to the misuse the available public funds, to poor investment decisions, and to inefficient risk management practice. Current HSID methods suffer from issues like underreporting of minor injury and property damage only (PDO) crashes, challenges of accounting for crash severity into the methodology, and selection of a proper safety performance function to model crash data that is often heavily skewed by a preponderance of zeros. Addressing these challenges, this paper proposes a combination of a PDO equivalency calculation and quantile regression technique to identify hot spots in a transportation network. In particular, issues related to underreporting and crash severity are tackled by incorporating equivalent PDO crashes, whilst the concerns related to the non-count nature of equivalent PDO crashes and the skewness of crash data are addressed by the non-parametric quantile regression technique. The proposed method identifies covariate effects on various quantiles of a population, rather than the population mean like most methods in practice, which more closely corresponds with how black spots are identified in practice. The proposed methodology is illustrated using rural road segment data from Korea and compared against the traditional EB method with negative binomial regression. Application of a quantile regression model on equivalent PDO crashes enables identification of a set of high-risk sites that reflect the true safety costs to the society, simultaneously reduces the influence of under-reported PDO and minor injury crashes, and overcomes the limitation of traditional NB model in dealing with preponderance of zeros problem or right skewed dataset.
Keyword Empirical Bayes'
Hot spots
Negative binomial regression
Network screening
Non-parametric models
Quantile regression
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Civil Engineering Publications
 
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Created: Tue, 07 Mar 2017, 16:06:35 EST by Jeannette Watson on behalf of Learning and Research Services (UQ Library)