Identification of High-Risk Intersections in an Urban Street Network Using Local and Highway Safety Manual Crash Prediction Models

Mariwan Askander Abdulla; Hardy Kamal  Karim

doi:10.24017/science.2025.2.10

Authors

Mariwan Askander Abdulla Civil Engineering Department, College of Engineering, University of Sulaimani, Sulaymaniyah, Iraq https://orcid.org/0009-0000-8924-9619
Hardy Kamal Karim Civil Engineering Department, College of Engineering, University of Sulaimani, Sulaymaniyah, Iraq https://orcid.org/0009-0004-5048-4207

Abstract

Nowadays, highway safety is a vital issue because vehicle crashes cause tremendous human, economic, social, and environment losses. This study asses intersections’ safety performance in Sulaimani urban street network where the number of vehicles has been rapidly growing, as the case study. Crash prediction models were developed and applied to assess the safety performance of the intersections. The crash data were reported from Sulaimani traffic police station, happened from January 2020 to September 2024. Besides the crash prediction models mentioned in the Highway Safety Manual (HSM), local crash prediction models for each selected intersections were developed, then the models were used as tools for assessing intersections safety performance. To know the intersections risk levels, five safety performance approaches were used namely Level of Safety Service, Excess Porengicted Average Crash Frequency using Safety Performance Function, Expected Average Crash Frequency with Empirical Bayes (EB) Adjustment, Equivalent Property Damage Only with EB Adjustment, and Excess Expected Average Crash Frequency with EB Adjustments. The results indicate that the local prediction model has a higher R² than the HSM model, indicating a better fit to the local traffic and road conditions specifically at four-leg signalized intersections, the local model achieved an R² value of 0.618, which is substantially higher than the 0.208 obtained from the HSM models. Moreoveresults show that four-leg signalized intersections have significantly higher crash rates, with 15 intersections identified as high-risk across both models. The findings offer practical insights for prioritizing safety improvements and resource allocation to enhance traffic safety in urban areas.

Keywords:

High-Risk Intersections, Safety Performance of Urban Intersection, Local Crash Prediction Models, Sulaimani Urban Road Net-work

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