Assessing Critical Road Sections: A Decision Matrix Approach Considering Safety and Pavement Condition

Asnake Adraro Angelo (), Kotaro Sasai and Kiyoyuki Kaito
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Asnake Adraro Angelo: Department of Civil Engineering, Division of Global Architecture, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita 565-0871, Japan
Kotaro Sasai: Department of Civil Engineering, Division of Global Architecture, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita 565-0871, Japan
Kiyoyuki Kaito: Department of Civil Engineering, Division of Global Architecture, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita 565-0871, Japan

Sustainability, 2023, vol. 15, issue 9, 1-20

Abstract: Identifying critical road sections that require prompt attention is essential for road agencies to prioritize monitoring, maintenance, and rehabilitation efforts and improve overall road conditions and safety. This study suggests a decision matrix with a hierarchical structure that factors in the pavement deterioration rate, infrastructure safety, and crash history to identify these sections. A Markov mixed hazard model was used to assess each section’s deterioration rate. The safety of the road sections was rated with the International Road Assessment Program star rating protocol considering all road users. Early detection of sections with fast deterioration and poor safety conditions allows for preventive measures to be taken and to reduce further deterioration and traffic crashes. Additionally, including crash history data in the decision matrix helps to understand the possible causes of a crash and is useful in developing safety policies. The proposed method is demonstrated using data from 4725 road sections, each 100 m, in Addis Ababa, Ethiopia. The case study results show that the proposed decision matrix can effectively identify critical road sections which need close attention and immediate action. As a result, the proposed method can assist road agencies in prioritizing inspections, maintenance, and rehabilitation decisions and effectively allocate budgets and resources.

Keywords: critical road sections; decision matrix; pavement deterioration; road safety; traffic crash; road maintenance; mixed Markov hazard model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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