ClusterHopper: Cross-region order dispatching optimization for ride-hailing drivers

Current ride-hailing platforms operate in isolation, forcing drivers to choose between income stability and maximum earnings. This fragmented approach leads to inefficient resource allocation, with 30%–40% of driver time spent idle in low-demand areas while nearby regions experience order backlogs. We present ClusterHopper, a multi-region dispatching platform that coordinates orders across competing regions while preserving the platform autonomy for each region. By modeling each region as an independent matching queue and implementing a two-tier optimization framework based on the network flow principle with future revenue projection for order dispatch across different regions, our solution addresses four critical industry challenges in an incremental fashion for optimal global resource allocations: (1) System receipt revenue, (2) Quantity of completed orders, (3) Average waiting time, and (4) Average revenue. Compared to a single platform, the multi-platform approach (across four platforms) improved the four metrics by 47.05%, 31.24%, 61.36%, and 13.12%, respectively. Compared to ClusterHopper in its ride-hailing mode, its ride-sharing extension achieves improvements of 334.08%, 313.79%, 40.30%, and 3.55% across the four key metrics. In addition, the order cancellation rate was reduced by 68.35%. Real-world simulations using Didi’s operational data demonstrate consistent performance across varying demand patterns, proving the viability of cooperative competition in mobility markets. © 2025 Elsevier Ltd.