Prioritizing highway safety improvement projects using a stochastic optimization model with robust constraints

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dc.authoridTirkolaee, Erfan Babaee/0000-0003-1664-9210
dc.authorwosidTirkolaee, Erfan Babaee/U-3676-2017
dc.contributor.authorDadashi, Ali
dc.contributor.authorMirbaha, Babak
dc.contributor.authorAtan, Zumbul
dc.contributor.authorTirkolaee, Erfan Babaee
dc.date.accessioned2024-05-19T14:46:17Z
dc.date.available2024-05-19T14:46:17Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractRoad authorities need efficient tools to assign a limited budget to safety improvement projects. The process of prioritizing safety improvement projects needs to predict the benefits and costs of projects. In real-life situations and also because of variances of crash frequency, crash modification factor (CMF), and imposed expenses, the forecast of project costs/benefits would be highly affected by uncertainty. Hence, this work develops a model based on stochastic binary programming with robust constraints to treat the inherent uncertainties to prioritize road safety improvement projects. Robust optimization approach ensures a high probability of feasibility for the solutions obtained under uncertain conditions. Efficiency of the suggested model is assessed using data collected from a real case study. Numerical results reveal that neglecting stochastic analysis would lead to a profit loss up to 15% of the total benefits of safety plan and the importance of considering stochastic nature of the problem increases as the budget of a safety plan decreases. Furthermore, to make a comparative analysis, the developed methodology is compared with some conventional methods, e.g., integer programming model and incremental benefit-cost analysis. Main findings demonstrate some deviations concerning how each approach copes with uncertainty which leads to differences in the list of the selected projects with respect to budget limitation. Finally, the developed methodology is recommended to managers as a flexible and robust tool to assess and select projects through setting the level of robustness against cost data uncertainty, which is done according to the decision-makers' attitudes.en_US
dc.identifier.doi10.1007/s00500-023-09255-w
dc.identifier.issn1432-7643
dc.identifier.issn1433-7479
dc.identifier.scopus2-s2.0-85173730535en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org10.1007/s00500-023-09255-w
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5489
dc.identifier.wosWOS:001081833700001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofSoft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectPrioritizingen_US
dc.subjectRoad Safety Investmenten_US
dc.subjectResource Allocationen_US
dc.subjectStochastic Optimizationen_US
dc.subjectRobust Optimizationen_US
dc.titlePrioritizing highway safety improvement projects using a stochastic optimization model with robust constraintsen_US
dc.typeArticleen_US

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