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地球信息科学学报 >

2025 , Vol. 27 >Issue 8: 1822 - 1840

DOI: https://doi.org/10.12082/dqxxkx.2025.250144

地球信息科学理论与方法

基于多源时空数据的城市公交站点地理空间优化方法：冗余优化模型

  • 李霄 , 1, 3, 4, 5 ,
  • 王少华 , 1, 2, * ,
  • 梁浩健 1, 2 ,
  • 周亮 3, 4, 5 ,
  • 刘畅 1, 2 ,
  • 王润桥 3, 4, 5 ,
  • 苏澄 1, 2
展开
  • 1.中国科学院空天信息创新研究院 遥感与数字地球全国重点实验室，北京 100101
  • 2.中国科学院大学，北京 101408
  • 3.兰州交通大学测绘与地理信息学院，兰州 730070
  • 4.地理国情监测技术应用国家地方联合工程研究中心，兰州 730070
  • 5.甘肃省测绘科学与技术重点实验室，兰州730070
*王少华（1983— ），男，陕西宝鸡人，博士，研究员，主要从事时空大数据分析、遥感智能计算、地理空间智能和地理空间优化研究。E-mail:

作者贡献：Author Contributions

李霄、王少华、梁浩健参与方法和实验设计；王少华、周亮提供实验数据；刘畅、王润桥、苏澄完成实验操作；李霄、王少华、梁浩健参与论文的写作和修改。所有作者均阅读并同意最终稿件的提交。

The study was designed by LI Xiao, WANG Shaohua, and LIANG Haojian. The experimental operation was completed by WANG Shaohua and ZHOU Liang. The experimental operation was completed by LIU Chang, WANG Runqiao, and SU Cheng. The manuscript was drafted and revised by LI Xiao, WANG Shaohua, and LIANG Haojian. All the authors have read the last version of paper and consented for submission.

李 霄（1999— ），男，四川广安人，硕士生，主要从地理空间优化研究。E-mail:

收稿日期: 2025-03-27

  修回日期: 2025-04-08

  网络出版日期: 2025-07-23

基金资助

国家重点研发计划项目(2023YFF0805904)

国家自然科学基金项目(42471495)

收起

Geospatial Optimization of Urban Bus Stops Based on Multi-Source Spatio-temporal Data: A Redundancy Optimization Model

  • LI Xiao , 1, 3, 4, 5 ,
  • WANG Shaohua , 1, 2, * ,
  • LIANG Haojian 1, 2 ,
  • ZHOU Liang 3, 4, 5 ,
  • LIU Chang 1, 2 ,
  • WANG Runqiao 3, 4, 5 ,
  • SU Cheng 1, 2
Expand
  • 1. State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
  • 2. University of Chinese Academy of Sciences, Beijing 101408, China
  • 3. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
  • 4. National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China
  • 5. Key Laboratory of Science and Technology in Surveying & Mapping Gansu Province, Lanzhou 730070, China
*WANG Shaohua, E-mail:

Received date: 2025-03-27

  Revised date: 2025-04-08

  Online published: 2025-07-23

Supported by

National Key Research and Development Program of China(2023YFF0805904)

National Natural Science Foundation of China(42471495)

Fold

摘要

【目的】可持续发展是全球各国发展的核心议题，涵盖了可持续的交通体系、包容和可持续的城市化等重要内容。作为城市公共服务设施的重要组成部分，公交网络是城市稳定运行的基石，其站点与线路的分布直接影响居民的出行方式。现有研究多聚焦于公交站点与线路的可达性分析、选址优化以及与人口、土地利用等因素的空间耦合关系，但在面对城市空间异质性和设施冗余问题时，仍存在优化深度不足、影响机制不清等问题。【方法】本文以北京市为例，重点关注北京东城区、西城区，本研究基于公交网络、地形、经济等多源数据，构建影响因素体系，并采用XGBoost机器学习方法，揭示驱动因子对公交站点分布的影响权重。在此基础上，提出了考虑站点冗余的数学模型，优化上下行站点的空间布局，绘制北京市公交站点空间优化布局图。【结果】研究结果表明： ① 北京市公交设施分布存在不均衡现象，中心城区与边缘区域在便捷公共交通可达人口比例上相差超过30%； ② 在19类影响因素中，人口密度为核心驱动因子，占比27.77%，风景名胜数量和停车场数量的影响较小，特征重要性不足0.5%； ③ 与p-中值模型相比，所提出的冗余优化模型显著减少了优化后站点的冗余程度，同时兼顾了加权距离最小化的性能，优化后的站点布局沿着原有公交线路分布且更加均匀。【结论】该研究结果可以为公交站点及其他公共服务设施布局提供一定的参考与理论支撑，有助于提升公共资源利用效率，促进城市可持续发展。

关键词： 可持续交通; 地理空间优化; 空间特征分析; 步行可达性; 冗余优化模型; 公交站点; 多源时空数据

本文引用格式

李霄 , 王少华 , 梁浩健 , 周亮 , 刘畅 , 王润桥 , 苏澄 . 基于多源时空数据的城市公交站点地理空间优化方法：冗余优化模型[J]. 地球信息科学学报, 2025 , 27(8) : 1822 -1840 . DOI: 10.12082/dqxxkx.2025.250144

Abstract

[Objectives] Sustainable development is an important issue for countries worldwide, encompassing key aspects such as sustainable transportation systems and inclusive, sustainable urbanization. As a crucial component of urban public service infrastructure, the public transportation network serves as a cornerstone of a city's stable operation, with the distribution of its stops and routes directly influencing residents' travel patterns. However, existing studies mainly focus on accessibility analysis, site selection optimization, and spatial coupling with factors such as population and land use, while lacking in-depth optimization approaches and clear mechanisms that address spatial heterogeneity and facility redundancy. [Methods] Taking Beijing as a case study, with a focus on Dongcheng and Xicheng Districts, this study constructs a system of influencing factors based on multi-source data, including public transportation networks, topography, and economic indicators, and employs the XGBoost machine learning method to reveal the impact weights of these driving factors on the distribution of bus stops. On this basis, a mathematical model incorporating stop redundancy is proposed to optimize the spatial layout of upstream and downstream stops, producing a spatial optimization map of bus stops in Beijing. [Results] The findings indicate that: (1) There is an imbalance in the distribution of public transportation facilities in Beijing, with the proportion of the population having convenient access to public transportation differing by more than 30% between central and peripheral urban areas. (2) Among the 19 influencing factors, population density is the key driving factor, accounting for 27.77%, while the number of scenic spots and parking facilities have minimal impact, with feature importance scores below 0.5%. (3) Compared to the p-median model, the proposed redundancy optimization model significantly reduces the redundancy of optimized stops while maintaining performance in minimizing weighted distance. The optimized stop layout is more evenly distributed along existing bus routes. [Conclusions] These findings provide valuable reference and theoretical support for the layout of bus stops and other public service facilities, contributing to the efficient utilization of public resources and promoting sustainable urban development.

Key words： sustainable transportation; geospatial optimization; spatial feature analysis; walking accessibility; redundancy optimization model; bus stops; multi-source spatiotemporal data

利益冲突：Conflicts of Interest 所有作者声明不存在利益冲突。

All authors disclose no relevant conflicts of interest.

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