An innovative data-driven integrated solution for ridesharing and dynamic vehicle routing

The increasing demand for flexible and sustainable mobility services has intensified interest in ridesharing and mobility-on-demand systems. However, current solutions remain limited, as they often neglect critical factors such as demand forecasting, vehicle repositioning, workload balance, and real-time adaptability. This study proposes an integrated data-driven framework for ridesharing and dynamic vehicle routing, composed of a periodic and dynamic components. By synchronising these stages, the system captures interdependencies between demand prediction and routing decisions, reducing inefficiencies caused by treating them separetely. The approach was validated on over 300,000 historical trips and tested with 9,016 real-time demands. Results show that the framework achieves a demand cancellation rate below 9%, balances driver workloads, and responds to new requests within 4 s. Sensitivity analyses reveal that omitting clustering or forecasting raises costs, while the full integration yields 6–10% savings. The proposed system provides a holistic, scalable, and sustainable real time optimisation model for future urban mobility.