RTSKG: Building a Rail Transit Station Knowledge Graph Dataset

2026-08-11

AI Summary

This study addresses the limitations of existing urban rail transit station analyses, which often fail to effectively model the complex interactions among heterogeneous urban entities. To bridge this gap, the work presents RTSKG—the first city-scale knowledge graph specifically designed for rail transit stations—alongside a unified framework for modeling heterogeneous entities. By integrating multi-source data such as stations, roads, and points of interest, RTSKG explicitly captures their spatial and semantic relationships and is published as Linked Data. Empirical evaluations demonstrate that RTSKG significantly enhances model performance in downstream tasks, including shop recommendation around stations and knowledge-augmented passenger flow prediction, thereby enabling more granular and informed urban rail transit analysis.

Abstract

Rail transit systems play a vital role in urban mobility and economic development. As key components of such systems, rail transit stations function as critical transport hubs that enhance urban accessibility and stimulate development in surrounding areas. City-level rail transit station related tasks (e.g., ridership prediction) require large-scale urban data, but current studies often neglect complex interactions among various urban entities in terms of data organization. In this paper, to address the above issue, we build a Rail Transit Station Knowledge Graph (RTSKG) dataset which explicitly models the spatial and semantic interactions among different kinds of urban entities, to benefit city-level rail transit station related tasks. RTSKG integrates heterogeneous urban entities, such as rail transit stations, road segments, and points of interest, with a specially designed unified schema, and is accessible as Linked Data at https://w3id.org/rtskg/. Evaluations on station-area store recommendation and knowledge-enhanced ridership prediction demonstrate the effectiveness of RTSKG, highlighting its potential to support city-level rail transit station analysis.

Problem

• rail transit station
• urban entities
• spatial interaction
• semantic interaction
• knowledge graph

Innovation

• Knowledge Graph
• Rail Transit Station
• Urban Entity Interaction
• Linked Data
• Spatial-Semantic Modeling