A before-after evaluation of protected right-turn signal phasings by applying Empirical Bayes and Full Bayes approaches with heterogenous count data models. (January 2023)

• Record Type: Journal Article
• Authors: Howlader, Md Mohasin; Yasmin, Shamsunnahar; Bhaskar, Ashish; Haque, Md Mazharul
• Is Part Of: Accident analysis and prevention. Volume 179(2023)
• Publication Date: 2023-01
• Subjects: Protected right-turn -- Crash modification factor -- Empirical bayes -- Full bayes -- Random parameters model; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2022.106882
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: Heterogenous count data models in before-after evaluation approaches improve the precision of safety estimates. Protected right-turn signals at cross and T intersections reduce about 87% and 91% of right-turn crashes, respectively. Protected right-turn signals have no detrimental effects on rear-end crashes. Simulation Bayes evaluation approaches offer in-depth insights into the crash modification factors. Abstract: Right-turn crashes (or left-turn crashes for the US or similar countries) represent over 40 % of signalized intersection crashes in Queensland, Australia. Protected right-turn phasings are a widely used countermeasure for right-turn crashes, but the research findings on their effects across different crash types and intersection types are not consistent. Methodologically, the Empirical Bayes and Full Bayes techniques are generally applied for before-after evaluations, but the inclusion of heterogeneous models within these techniques has not been considered much. Addressing these research gaps, the objective of this study is to evaluate the effectiveness of protected right-turn signal phasings at signalized intersections employing heterogeneous count data models with the Empirical Bayes and Full Bayes techniques. In particular, the Empirical Bayes approach based on random parameters Poisson-Gamma models (simulation-based Empirical Bayes), and the Full Bayes approach based on random parameters Poisson-Lognormal intervention models (simulation-based Full Bayes) are applied. A total of 69 Cross intersections (with ten treated sites) and 47 T intersections (with six treated sites) from Southeast Queensland in Australia were included in the analysis to estimate the effects of protected right-turn signal phasings on various crash types. Results show that the change of signal phasing from a permissive right-turn phasing to the protected right-turn phasing at cross and T intersections reduces about 87 % and 91 % of right-turn crashes, respectively. In addition, the effect of protected right-turn phasings on rear-end crashes was not significant. The heterogenous count data models significantly address extra Poisson variation, leading to efficient safety estimates in both simulation-based Empirical Bayes and simulation-based Full Bayes approaches. This study demonstrates the importance of accounting for unobserved heterogeneity for the before-after evaluation of engineering countermeasures.