A before-after evaluation of protected right-turn signal phasings by applying Empirical Bayes and Full Bayes approaches with heterogenous count data models
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Description
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.
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ID Code: | 236161 | ||||||
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Item Type: | Contribution to Journal (Journal Article) | ||||||
Refereed: | Yes | ||||||
ORCID iD: |
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Measurements or Duration: | 24 pages | ||||||
Keywords: | Protected right-turn, Crash modification factor, Empirical Bayes, Full Bayes, Random parameters model | ||||||
DOI: | 10.1016/j.aap.2022.106882 | ||||||
ISSN: | 0001-4575 | ||||||
Pure ID: | 117151451 | ||||||
Divisions: | Current > Research Centres > Centre for Behavioural Economics, Society & Technology Current > Research Centres > Centre for Data Science Current > Research Centres > Centre for Future Mobility/CARRSQ Current > QUT Faculties and Divisions > Faculty of Business & Law Current > QUT Faculties and Divisions > Faculty of Science Current > QUT Faculties and Divisions > Faculty of Engineering Current > Schools > School of Civil & Environmental Engineering Current > QUT Faculties and Divisions > Faculty of Health Current > Schools > School of Psychology & Counselling |
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Copyright Owner: | 2022 Elsevier Ltd. | ||||||
Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||||||
Deposited On: | 09 Nov 2022 01:14 | ||||||
Last Modified: | 13 May 2024 18:42 |
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