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The integration of electric vehicles (EVs) into public transit systems is crucial for enhancing sustainability and operational efficiency, particularly in rural tourism regions where demand is highly variable and infrastructure constraints pose unique challenges. Traditional transportation planning approaches often lack the adaptability required to handle the fluctuating nature of tourist mobility, leading to inefficiencies in service coverage and resource utilization. While fuzzy logic-based models have been extensively applied in urban transit optimization, their applicability to rural EV public transit remains underexplored. This study addresses this gap by developing the Fuzzy-Artificial Multiple Intelligence System (F-AMIS), an enhanced version of the Artificial Multiple Intelligence System (AMIS). F-AMIS integrates new intelligence boxes and an optimized selection formula, allowing for real-time adaptive decision-making in EV bus networks. A real-world case study demonstrates that F-AMIS significantly outperforms conventional optimization methods, achieving a 20% reduction in operational costs and increasing service coverage from 75% to 90%, while also enhancing resilience and sustainability indices. These findings highlight the potential of F-AMIS as a scalable, intelligent optimization framework for improving the efficiency and sustainability of rural EV transit systems. Future research should explore integrating F-AMIS with advanced AI-driven decision models, refining fuzzy logic techniques for rural-specific constraints, and assessing the model’s adaptability across diverse global tourism networks to further enhance its applicability and impact.
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Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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