A Multi-Dimensional Sustainability Framework for Dynamic Ridesharing Platforms: Integrating Hybrid Optimization to Address Environmental, Social, and Economic Impacts

Urban mobility systems in emerging cities are increasingly strained by rising greenhouse gas emissions, inefficient vehicle utilization, and unsustainable travel demand patterns. Dynamic ridesharing, when guided by intelligent optimization, offers significant potential to reduce vehicle miles travelled (VMT), lower energy consumption, and curb urban emissions. This study introduces an environmentally focused hybrid multi-objective optimization framework that integrates evolutionary algorithms (NSGA-II and SPEA2) with machine learning models (Neural Networks and Reinforcement Learning) to optimize real-time ridesharing decisions under diverse urban demand conditions. Implemented in a MATLAB-based simulation environment, the framework captures realistic geographic and temporal variability in ride requests. Three urban demand scenarios low, medium, and high were analyzed to evaluate the system's responsiveness and adaptability. Core optimization components were modularized for scalability, and hybridization ensured balanced trade-offs across environmental and operational objectives. Simulation results demonstrated that the hybrid model outperformed both the baseline (no ridesharing) and traditional ridesharing setups, achieving up to 35% reduction in VMT, significant cost savings, and improved travel time control. In high-demand settings, the model further reduced system-wide trip costs while preserving operational fairness. Survey responses from over 100 participants indicated high public acceptance of environmentally sustainable ridesharing systems, particularly those emphasizing equity, affordability, and low-impact routing. The research underscores the importance of embedding sustainability criteria within urban mobility algorithms and offers a computationally efficient, behaviorally informed, and scalable model suited for African cities grappling with congestion, emissions, and transport equity. The proposed framework serves as a replicable tool for urban planners and mobility service providers aiming to balance environmental sustainability, economic efficiency, and social inclusion in the design of next-generation transport systems.