Shortest Path Planning in Multimodal Metropolitan Transportation Networks Under Timetable and Label Constraints

Abstract

This study proposes a label-setting shortest-path algorithm with timetable and label constraints to address the path-planning problem in multimodal urban agglomeration transportation networks. The proposed algorithm addresses the limitations of traditional shortest-path methods, which are often challenged by complex conditions, including transfer constraints, timetable dependencies, and restrictions on the number of transfers. By analyzing the characteristics of urban agglomeration trip chains, this study constructed a multimodal network model incorporating six transportation modes: walking, buses, intercity coaches, metro, intercity railways, and private vehicles. A deterministic finite automaton was introduced to constrain feasible mode sequences, ensuring that path planning aligns with real-world travel patterns. The algorithm incorporates time-window constraints to simulate the effects of static timetables in scheduled transportation modes, such as railways and coaches. By improving the label-correcting algorithm via topological sorting and applying state-dominance rules to reduce redundant computations, it achieves optimal path planning under multiple constraints. Case studies demonstrate that the algorithm effectively balances transfer frequency and travel cost, reducing total cost by approximately 5% while ensuring feasibility, thereby validating the synergistic advantages of multimodal transportation networks. Therefore, the proposed algorithm can theoretically support multimodal traffic selection behavior or mixed traffic network modeling.