发表机构
School of Computer Science and Engineering, Southeast University; Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education; School of Architecture, Southeast University（东南大学计算机科学与工程学院; 教育部新一代人工智能技术及其交叉应用重点实验室; 东南大学建筑学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对城市级轨道交通站点任务数据组织中忽视城市实体交互的问题，构建RTSKG数据集并验证其在商铺推荐和客流量预测中的有效性，可支撑城市级轨道交通站点分析。
AI 中文摘要
轨道交通系统在城市交通和经济发展中发挥着至关重要的作用。作为此类系统的关键组成部分，轨道交通站点是重要的交通枢纽，可提升城市可达性并带动周边区域发展。城市级轨道交通站点相关任务（如客流量预测）需要大规模城市数据，但现有研究在数据组织方面往往忽视各类城市实体间的复杂交互。为解决上述问题，本文构建了轨道交通站点知识图谱（Rail Transit Station Knowledge Graph，RTSKG）数据集，该数据集明确建模不同类型城市实体间的空间与语义交互，以助力城市级轨道交通站点相关任务。RTSKG 采用专门设计的统一模式整合了轨道交通站点、道路路段、兴趣点等异构城市实体，可作为关联数据通过指定 URL 获取。对站点区域商铺推荐和知识增强型客流量预测的评估验证了 RTSKG 的有效性，凸显其支持城市级轨道交通站点分析的潜力。
英文摘要
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.
Comments21 pages, Accepted by ISWC 2026
