Article detail · 2026
A systematic risk assessment approach for urban roadside infrastructure assets
- Year
- 2026
- Type
- article
Data source split
- YÖKSİS YÖKSİS article record
- YÖKSİS venue Structure and Infrastructure Engineering
- Catalog match (ISSN) Structure and Infrastructure Engineering
- OpenAlex OpenAlex enrichment (abstract, citations, topics)
Abstract
OpenAlex · English
Revealing potential risks and the influence of these risks on urban roadside vulnerable/risky zones has been a great deal for the last decades owing to increasing vehicular mobility. This study aimed to develop a risk assessment methodology for urban roadside infrastructure assets. In this context, seven risk parameters that are likely to affect hazardous aboveground assets installed on urban roadsides were determined, and weights of each parameter (order of importance) were presented based on expert surveys and literature information using the Analytic Hierarchy Process (AHP) method. Mathematical models were set up for each risk parameter using real field data, The American Association of State Highway and Transportation Officials (AASHTO), literature, related standards, and expert surveys by linear, nonlinear, and binary logistic regression analyses. Risk groups were created, and precautions were offered. The efficiency of the models was verified based on the 28 accident records and field observations in 172 assets. When considering the assets that undergo traffic accidents, 71% of the total assets are ranked in the critical and high-risk group. This ratio is consistent with the risk group definitions assigned in this study; thus, the model proposed is accurate and can be applied reliably. The verification process of the total risk model was sufficiently successful to be used in practice and can be generalized by considering the road and traffic characteristics, infrastructure facilities, and future requirements of the regions or countries that reflect similar historical and cultural concerns. Practitioners, governmental institutes, and researchers can apply this methodology to provide their own data with high transparency and reliability.
Topics
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