International Journal of Science, Engineering and TechnologyAuthors: Jayashri Waman, Pratik Pawar, Shruti Dalvi, Rohan Mane, Anita Mahajan
Abstract: Urban mobility systems are facing increasing pressure due to traffic congestion, rising fuel consumption, and growing environmental concerns. The transportation sector alone contributes a significant share of global greenhouse gas emissions, making sustainable mobility solutions a critical requirement [1]. Over the past decade, researchers have proposed several optimization-based routing models, including the Pollution Routing Problem (PRP) [5], fuel-consumption-aware vehicle routing approaches [4], and emission-sensitive routing frameworks [3]. However, the majority of these efforts have focused primarily on freight and logistics operations rather than passenger transportation. Advances in time-dependent routing models [10] and environmentally conscious vehicle routing formulations [6], [7] have improved operational efficiency and emission control in logistics networks. Nevertheless, passenger-oriented solutions such as smart carpooling and ridesharing remain relatively underexplored from a carbon-optimization perspective, despite their potential to significantly reduce vehicle usage in urban environments. To address this gap, this paper proposes AUMO, an AI-powered Urban Mobility Optimizer designed to integrate carbon-aware routing with smart carpooling for passenger mobility. The proposed framework adapts established low-carbon routing principles from freight transportation [3], [6] and applies them to urban passenger travel scenarios. A simulation-based evaluation using a microscopic traffic environment demonstrates that the proposed system achieves notable reductions in CO₂ emissions and fleet size while improving vehicle occupancy and overall travel efficiency. These results highlight the potential of intelligent carpooling systems for sustainable urban mobility.
DOI: http://doi.org/10.5281/zenodo.20728800
