Dynamic pick-up point recommendation with multi-modal deep forest and incentive-based adaptive Kuhn-Munkres Algorithm

Recommendations for optimal pick-up points significantly enhance service efficiency, reduce economic and temporal costs, and alleviate traffic congestion. However, spatiotemporal imbalance between ride-hailing supply and passenger demand presents significant challenges. Current models often overlook critical influencing factors such as passenger satisfaction, travel environment, and travel cost factors. Moreover, solution algorithms, including exact algorithms and heuristics, struggle to achieve global optimality and computational efficiency in large-scale scenarios. This study introduces a comprehensive mathematical model that incorporates four key influencing factors: passenger walking distance, passenger waiting time, traffic conditions, and estimated ride-hailing fare. The solution approach consists of a novel pick-up point evaluation algorithm and an incentive-based adaptive Kuhn-Munkres matching algorithm. The evaluation algorithm employs a multi-modal decision tree structure, enhanced by deep learning techniques to improve the accuracy of pick-up point evaluations. The matching algorithm features a multi-scenario adaptive mechanism that dynamically adjusts edge weights and selects optimal edges for augmentation under various conditions and strategies, thereby ensuring globally optimal matching of passengers and pick-up points. Extensive experiments on large-scale real-world datasets validate the superior performance of the evaluation and matching algorithms, especially in handling large-scale instances. The developed model and algorithms assist ride-hailing platforms in optimizing operations, enhancing service quality, increasing profitability, and improving cost management.