Simulation assessment of autonomous electric vehicles on urban sustainability in Riyadh City.

Louati, A; Louati, H; Kariri, E · 2026 · PubMed Central

DOI: 10.1038/s41598-026-40646-9

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Summary

This study addresses the lack of localized, monetized frameworks for assessing the sustainability impacts of autonomous battery electric vehicles (BEVs) in Gulf urban environments. While prior research has extensively evaluated autonomous vehicle (AV) externalities in Western contexts, there is a significant gap in evidence specific to Saudi Arabia’s unique climatic, infrastructural, and economic conditions. Motivated by the need to support Saudi Vision 2030’s smart city goals, the authors aim to quantify the environmental, social, and economic externalities of AV-BEVs in Riyadh using national data. The research seeks to provide policymakers with a validated, policy-ready framework to evaluate the real-world benefits of AV deployment, specifically focusing on accident risks, congestion, emissions, urban land use, and noise exposure. The methodology integrates a static monetized external cost model with dynamic microscopic traffic simulation. The authors adapted the Letmathe and Paegert framework, originally developed for Germany, by recalibrating all parameters using 2023–2024 datasets from Saudi institutions, including the Ministry of Transport, the General Authority for Statistics, and the Saudi Energy Efficiency Center. The study compares two scenarios: human-driven BEVs (baseline) and autonomous BEVs. External costs were quantified using established transport economics models, such as a modified Smeed’s Law for accident frequency and the Bureau of Public Roads (BPR) function for congestion delays, with all values expressed in Saudi Riyals (SAR). To validate the static model, the authors conducted a dynamic simulation using SUMO on Prince Mohammed Bin Salman Road in Riyadh, incorporating real hourly traffic data to model both interrupted (signalized arterial) and uninterrupted (freeway) flow conditions. The results indicate that autonomous BEVs reduce total external costs by 1.138 SAR per vehicle-kilometer, representing a 39.4% decrease compared to human-driven BEVs. Specific reductions include a 55% drop in accident costs, a 40% reduction in land-use costs, and a 25% decrease in congestion costs. In the baseline comparison, emission costs showed no change because both scenarios utilized battery electric vehicles with identical grid electricity factors; however, the study notes that environmental gains would increase under cleaner-grid scenarios. The dynamic simulation corroborated these findings, demonstrating lower delays and more stable traffic flow under AV operation, particularly on signalized arterials where start-up lost time and headway variance were reduced. The significance of this work lies in its provision of the first localized, monetized sustainability assessment for autonomous mobility in the Gulf region. By extending externality modeling to a non-Western context, the study offers a transferable methodology for emerging economies transitioning toward autonomous transport. The findings provide actionable policy recommendations aligned with Saudi Vision 2030, including the need for data-sharing frameworks, parking and curb management reforms, and urban zoning adjustments to maximize the sustainability benefits of high-density AV deployment in cities like Riyadh.

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