构建城市轨道交通站点知识图谱，助力交通与商业分析
RTSKG: Building a Rail Transit Station Knowledge Graph Dataset
- 设计统一模式整合车站、道路、兴趣点等多元城市实体
- 在门店推荐与客流预测任务中验证数据有效性
- 开放可查的链接数据，适合城市规划与智能交通研究者
轨道交通系统在城市交通与经济发展中至关重要。作为关键枢纽，轨道交通站点提升城市可达性并带动周边发展。现有研究在开展城市级站点相关任务（如客流预测）时，常忽视多类城市实体间复杂交互关系的数据组织方式。本文构建了轨道交通站点知识图谱（RTSKG）数据集，显式建模不同城市实体间的空间与语义关联，以支持城市级站点相关任务。RTSKG采用专用统一模式整合轨道交通站点、道路段、兴趣点等异构实体，可通过 https://w3id.org/rtskg/ 以链接数据形式获取。在站点周边商铺推荐与知识增强型客流预测任务上的评估表明其有效性，凸显其在城市轨道交通分析中的潜力。
原文摘要 · Abstract (English)
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.
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