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<p>This study presents a localized sustainability assessment of autonomous battery electric vehicles (BEVs) in Saudi Arabian urban environments using a monetized external cost framework that covers accidents, congestion, emissions, urban land use, and noise exposure. The study contributes theoretically by extending AV externality modeling to a Gulf-specific context and practically by providing a policy-ready framework aligned with Saudi Vision 2030. Using 2023-2024 national datasets from the Ministry of Transport, the General Authority for Statistics, and the Saudi Energy Efficiency Center within a three-pillar evaluation model (environmental, economic, and social), we find that autonomous BEVs reduce total external costs by 1.138 SAR per vehicle-kilometer (39.4%) compared with human-driven BEVs, with reductions of 55% in accident costs, 40% in land-use costs, and 25% in congestion costs. In the baseline BEV-to-BEV comparison, fuel-combustion emissions are zero and grid-electricity emissions are held constant, so emissions show no change; environmental gains increase under cleaner-grid scenarios. A dynamic microscopic simulation on Prince Mohammed Bin Salman Road in Riyadh corroborates the reductions through lower delays and more stable flow. The framework is parameterized by automation levels and market penetration and supports policy recommendations including data-sharing frameworks and parking and curb management reforms for high-density Saudi cities.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月17日

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  • Ali Louati,
  • Hassen Louati,
  • Elham Kariri

摘要

This study presents a localized sustainability assessment of autonomous battery electric vehicles (BEVs) in Saudi Arabian urban environments using a monetized external cost framework that covers accidents, congestion, emissions, urban land use, and noise exposure. The study contributes theoretically by extending AV externality modeling to a Gulf-specific context and practically by providing a policy-ready framework aligned with Saudi Vision 2030. Using 2023-2024 national datasets from the Ministry of Transport, the General Authority for Statistics, and the Saudi Energy Efficiency Center within a three-pillar evaluation model (environmental, economic, and social), we find that autonomous BEVs reduce total external costs by 1.138 SAR per vehicle-kilometer (39.4%) compared with human-driven BEVs, with reductions of 55% in accident costs, 40% in land-use costs, and 25% in congestion costs. In the baseline BEV-to-BEV comparison, fuel-combustion emissions are zero and grid-electricity emissions are held constant, so emissions show no change; environmental gains increase under cleaner-grid scenarios. A dynamic microscopic simulation on Prince Mohammed Bin Salman Road in Riyadh corroborates the reductions through lower delays and more stable flow. The framework is parameterized by automation levels and market penetration and supports policy recommendations including data-sharing frameworks and parking and curb management reforms for high-density Saudi cities.