title: Propensity scores-potential outcomes framework to incorporate severity probabilities in the highway safety manual crash prediction algorithm.
abstract: Accurate estimation of the expected number of crashes at different severity levels for entities with and without countermeasures plays a vital role in selecting countermeasures in the framework of the safety management process. The current practice is to use the American Association of State Highway and Transportation Officials' Highway Safety Manual crash prediction algorithms, which combine safety performance functions and crash modification factors, to estimate the effects of safety countermeasures on different highway and street facility types. Many of these crash prediction algorithms are based solely on crash frequency, or assume that severity outcomes are unchanged when planning for, or implementing, safety countermeasures. Failing to account for the uncertainty associated with crash severity outcomes, and assuming crash severity distributions remain unchanged in safety performance evaluations, limits the utility of the Highway Safety Manual crash prediction algorithms in assessing the effect of safety countermeasures on crash severity. This study demonstrates the application of a propensity scores-potential outcomes framework to estimate the probability distribution for the occurrence of different crash severity levels by accounting for the uncertainties associated with them. The probability of fatal and severe injury crash occurrence at lighted and unlighted intersections is estimated in this paper using data from Minnesota. The results show that the expected probability of occurrence of fatal and severe injury crashes at a lighted intersection was 1 in 35 crashes and the estimated risk ratio indicates that the respective probabilities at an unlighted intersection was 1.14 times higher compared to lighted intersections. The results from the potential outcomes-propensity scores framework are compared to results obtained from traditional binary logit models, without application of propensity scores matching. Traditional binary logit analysis suggests that the probability of occurrence of severe injury crashes is higher at lighted intersections compared to unlighted intersections, which contradicts the findings obtained from the propensity scores-potential outcomes framework. This finding underscores the importance of having comparable treated and untreated entities in traffic safety countermeasure evaluations.
authors.affiliation: Institute for Transport Planning and Systems, Swiss Federal Institute of Technology, ETH Zurich, Wolfgang-Pauli Strasse 15, HIL F 41.1, 8093 Zurich, Switzerland. Electronic address: lekshmi.sasidharan@ivt.baug.ethz.ch.
authors.forename: Lekshmi
authors.initials: L
authors.lastname: Sasidharan
authors.affiliation: The Pennsylvania State University, Department of Civil and Environmental Engineering, 212 Sackett Building, University Park, PA 16802, United States. Electronic address: edonnell@engr.psu.edu.
authors.forename: Eric T
authors.initials: ET
authors.lastname: Donnell
mesh_terms: D000063:Accidents, Traffic / Q000401:mortality / Q000706:statistics & numerical data*; D000465:Algorithms; D004779:Environment Design / Q000706:statistics & numerical data*; D006801:Humans; D008029:Lighting / Q000706:statistics & numerical data*; D016015:Logistic Models; D057216:Propensity Score; D015599:Trauma Severity Indices
keywords: Causal model; Highway Safety Manual; Lighting; Potential outcome; Propensity score; Severity probability