Enhancing Carpooling’s Contribution to Sustainability through AI Integration

This research explores how Artificial Intelligence (AI) can transform carpooling into a more efficient, sustainable, and user-friendly transportation model. AI-driven systems address logistical and behavioural challenges, making carpooling a key strategy for reducing carbon emissions and optimizing urban mobility. Through a scoping review, the study highlights four key areas where AI enhances carpooling: improved ride matching, dynamic routing, and real-time adjustments based on user preferences; environmental benefits through increased vehicle occupancy and reduced single-occupancy trips; user engagement via incentives and personalization; and integration with AI-powered public transport for seamless multi-modal commuting. Despite carpooling’s environmental advantages, adoption has been hindered by inefficiencies and low participation. Existing AI applications in transportation focus on demand prediction, behaviour analysis, and real-time optimization, but carpooling remains underexplored. This study aims to bridge that gap, providing actionable insights for redefining carpooling. The research offers policy recommendations to maximize AI-driven carpooling’s efficiency and environmental impact. It also proposes AI-managed incentives to boost user participation, promoting sustainable commuting. In conclusion, AI can revolutionize carpooling, turning it from a niche practice into a mainstream mobility option, ultimately contributing to smarter, greener cities.