数据驱动的城市群综合运输通道识别算法与特征分析

doi:10.16097/j.cnki.1009-6744.2025.03.007

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (3): 73-84.DOI: 10.16097/j.cnki.1009-6744.2025.03.007

数据驱动的城市群综合运输通道识别算法与特征分析

刘振国^1,2,3，齐崇楷¹，王江锋^*1,4，王亚飞¹

1. 北京交通大学，交通运输学院，北京100044；2.交通运输部科学研究院，北京100029；3.综合交通运输理论交通运输行业重点实验室，北京100029；4.北京交通大学，北京交通大学唐山研究院，河北唐山063000

收稿日期:2025-01-30 修回日期:2025-03-28 接受日期:2025-04-02 出版日期:2025-06-25 发布日期:2025-06-20
作者简介:刘振国(1985—)，男，山东菏泽人，正高级工程师，博士生。
基金资助:
国家重点研发计划(2023YFC3009604)；河北省省级科技计划项目(236Z0802G)。

Data-driven Identification Algorithm and Feature Analysis of Integrated Transport Corridors in Urban Agglomerations

LIU Zhenguo^1,2,3, QI Chongkai¹, WANG Jiangfeng^*1,4, WANG Yafei¹

1. School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China; 2. China Academy of Transportation Science, Beijing 100029, China; 3. Key Laboratory of Transport Industry of Comprehensive Transportation Theory, Beijing 100029, China; 4. Tangshan Research Institute of Beijing Jiaotong University, Beijing Jiaotong University, Tangshan 063000, Hebei, China

Received:2025-01-30 Revised:2025-03-28 Accepted:2025-04-02 Online:2025-06-25 Published:2025-06-20
Supported by:
National Key Research and Development Program of China (2023YFC3009604)；S&T Program of Hebei(236Z0802G)。

摘要/Abstract

摘要： 利用出行特征数据识别综合交通运输通道是合理布局城市群综合运输通道的关键技术。本文基于城市群手机信令数据，提出一种综合运输通道识别四阶段方法框架，即数据准备、运输方式划分、最短路径搜索和通道识别。在运输方式划分方面，提出一种以运输平均速度和站点POI(PointofInterest)位置为决策变量的高速铁路、普速铁路和公路多方式划分算法。在最短路搜索方面，设计一种基于双向A*算法的最短路径搜索算法。在通道识别方面，基于行政边界划分通道区段并以运输量为综合运输通道区段判别参数。以京津冀城市群为例进行实证分析，结果表明，本文方法能够有效处理城市群手机信令数据，并识别出6条综合运输通道，验证了方法的可行性和准确性。在案例数据下，京津冀城市群公路和铁路的运输量占比分别为81.87%和18.13%，公路的短程运输客流较铁路更多；节假日因素显著提高了综合运输通道的客流量，平均运输量增加62.6%，平均客流周转量提升61.2%。

关键词: 综合运输, 通道识别方法, K条渐短路径搜索算法, 手机信令数据, 京津冀城市群

Abstract: The rational layout of integrated transportation corridors is crucial for the coordinated development of multiple transportation modes within urban agglomerations. Utilizing travel characteristic data to identify integrated transportation corridors is a key problem that urgently needs to be addressed. Based on the mobile signaling data from urban agglomerations, this paper proposes an integrated transportation corridor identification framework which is suitable for various transportation modes. The framework consists of four components: data preparation, transportation mode classification, shortest path search, and corridor identification. This paper proposes a multi-modal division algorithm for high-speed rail, conventional rail, and road, with decision variables based on average transport speed and station POI locations. Additionally, a bidirectional A* algorithm is designed for shortest path search. Based on the obtained shortest paths and the spatial features of the road network, this study proposes an integrated transportation corridor identification algorithm through mobile signaling data. An empirical analysis is conducted using the Beijing-Tianjin-Hebei urban agglomeration as a case study. The corridor identification algorithm identifies 6 integrated transportation corridors to verify its effectiveness and availability. The identified two transportation modes: road and rail，account for 81.87% and 18.13%, respectively in terms of transportation volume. Compared to rail transport, road transport handles a larger share of short-distance passenger flows. Considering holiday travel characteristics, the presence of holiday factors significantly increases the passenger flow in the integrated transportation corridors, with average transportation volume rising by 62.6% and average passenger turnover increasing by 61.2%.

Key words: integrated transportation, corridors identification method, K-shortest path search algorithm, mobile phone signaling data, Beijing-Tianjin-Hebei region

中图分类号:

U111

刘振国, 齐崇楷, 王江锋, 王亚飞. 数据驱动的城市群综合运输通道识别算法与特征分析[J]. 交通运输系统工程与信息, 2025, 25(3): 73-84.

LIU Zhenguo, QI Chongkai, WANG Jiangfeng, WANG Yafei. Data-driven Identification Algorithm and Feature Analysis of Integrated Transport Corridors in Urban Agglomerations[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(3): 73-84.

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数据驱动的城市群综合运输通道识别算法与特征分析

Data-driven Identification Algorithm and Feature Analysis of Integrated Transport Corridors in Urban Agglomerations

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