Identifying the Multimodal Hierarchy of Public Transit Systems Using Trip Chain Data

2025-09-28

AI Summary

Macroscopic identification of complementary and competitive relationships among transport modes in multimodal public transit systems remains challenging. Method: This paper proposes an “ascend–descend” multimodal hierarchical framework, the first to characterize, at the system level, the co-evolutionary mechanisms between low-tier (high accessibility, e.g., walking) and high-tier (high efficiency, e.g., metro) modes within trip chains. Leveraging multi-source smart-card trajectory data, we integrate sequence pattern mining with hierarchical inference algorithms to automatically infer the macro-level ordinal ranking of transport modes. Results: Empirically validated in the Seoul Metropolitan Area, the method successfully uncovers a hierarchical structure consistent with real-world travel behavior. It provides an interpretable, transferable theoretical foundation and technical pathway for multimodal system planning, resource allocation, and integrated service design.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data
• Machine Learning: Multimodal Learning

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

As urban mobility integrates traditional and emerging modes, public transit systems are becoming increasingly complex. Some modes complement each other, while others compete, influencing users' multimodal itineraries. To provide a clear, high-level understanding of these interactions, we introduce the concept of a macroscopic multimodal hierarchy. In this framework, trips follow an "ascending-descending" order, starting and ending with lower hierarchical modes (e.g., walking) that offer high accessibility, while utilizing higher modes (e.g., subways) for greater efficiency. We propose a methodology to identify the multimodal hierarchy of a city using multimodal smart card trip chain data and demonstrate its application with actual data collected from Seoul and the surrounding metropolitan area in South Korea.

Problem

• Identifying multimodal transit hierarchy using trip chain data
• Analyzing interactions between complementary and competing transit modes
• Establishing ascending-descending order of transit modes for efficiency

Innovation

• Introducing macroscopic multimodal hierarchy concept
• Using trip chain data to identify transit hierarchy
• Applying ascending-descending order framework to mobility