关键词: 城市交通, 上车点推荐, 多目标优化, 帕累托最优, 综合收益

Abstract: Optimization of online ride-sharing pick-up points effectively reduces driver pick-up and passenger walking distances, enhances pick-up efficiency, alleviates passenger aggregation and node congestion, and improves passenger travel experience. However, the results of the pick-up point planning are influenced by various factors, and it is challenging to address these factors comprehensively to ensure the feasibility of the planning scheme. In response to these challenges, a multi-objective integer linear planning model is proposed, covering passenger and driver benefit as well as road condition. Moreover, a relaxed segmentation iterative algorithm and a Pareto front generation method based on dynamic constraints are developed. Firstly, the spatial and temporal trajectory data is analyzed and filtered to obtain potential pick-up points that are accessible on the road network. Secondly, the relaxation problem is partitioned into subproblems by the branch-and-bound method, which is solved by the simplex method to convert the objective function to its standard form. Thirdly, the single-objective models are solved separately to obtain the boundary values, which are dynamically adjusted as constraints to generate Pareto surfaces. Finally, the correlation among the objectives is analyzed through a large number of experiments to derive the equilibrium point, so as to effectively recommend pick-up points from different perspectives.

Key words: urban transportation, boarding point recommendation, multi-objective optimization, Pareto optimization, comprehensive benefit