Research Papers

Comparing Crash Prediction Techniques for Ranking of Sites in a Network Screening Process

Filename 6B-Thakali-FP.pdf
Filesize 397 KB
Version 1
Date added June 30, 2016
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Category 2016 CARSP XXVI Halifax
Tags Research and Evaluation, Session 6B, Student Paper Award Winner
Author/Auteur Lalita Thakali, Liping Fu, Tao Chen
Award/Prix Étudiant 1 Student
Stream/Volet Research and Evaluation

Slidedeck Presentation

6B - Thakali


Network screening, a process for an effective and efficient management of road safety programs,
relies on crash prediction techniques to quantify the relative risks of given sites. The two most
commonly used statistical approaches are- cross-sectional model-based approach and Empirical
Bayes (EB) approach as they are known for reducing regression-to-mean bias problem of a
simple crash history-based method. Meanwhile, relatively the EB approach is known to be a
robust as it accounts for a site-specific risk level while still incorporating the risk estimates
obtained from a cross-sectional model. Common to both the approaches is they are relatively
convenient to apply and easy to interpret due to a defined mathematical equation used to relate
crashes and the potential explanatory variables. However, pre-specification of such relations are
challenging, as the true cause-effects are not known in advance. One approach is to use trial and
error process to select for the final relation. Nonetheless, potential misspecification may still
remain which consequently could result in an inaccurate list of crash hotspots in a network
screening process. As an alternative to model-based approach, this study applies kernel
regression (KR), which is a data-driven nonparametric method. In addition, the KR method is
extended in a similar framework of EB approach to account for site-specific risk levels. All these
techniques when applied in a case study using crash data from Highway 401 of Ontario, Canada
showed that some deviations exist between the methods, particularly when applied in the ranking
of sites in a network screening process.

Lalita Thakali, Liping Fu, Tao Chen