文章编号： 1672-5328（2025）02-0106-11

谭泽芳1, 2, 3，周军1, 2，黄嘉俊1, 2，杨心怡1, 2，胡家琦1, 2
(1. 深圳市规划国土发展研究中心，广东深圳518040；2. 广东省城市规划与交通仿真决策工程技术研究中心，广 东深圳518040；3.东南大学交通学院，江苏南京211189)

摘要： 湾区是国家重要的经济增长极，作为测定经济发展、人员流动、货物流通等方面指标的重要 因素，在湾区日常运作过程中获取精准的跨界交通特征和规律尤为重要。通过总结国内外跨界交通 调查的发展历程，系统探讨湾区主要城市跨界交通调查的理论基础。总结城市规划管理对跨界交通 的调查需求，制定以经验评估和大数据新技术新方法评估贯穿主线的调查框架，提出样本量设计、 数据校核、数据扩样和大数据融合等具体调查方法。最后以深港跨界客流空间分布、深港跨界旅客 深圳侧接驳交通方式分布、融合手机信令和调查问卷数据的深圳机场旅客空间分布为例证，证实了 跨界交通调查方法的有效性和提高获取指标精度的可行性，为湾区、城市群、都市圈等区域的跨界 交通调查提供参考。

关键词： 交通规划；跨界交通调查；抽样技术；大数据方法；湾区；深圳市

中图分类号： U491.1+1

文献标识码：A

Theoretical and Technical Research on Cross-Border Transportation Surveys in Shenzhen

TAN Zefang1, 2, 3, ZHOU Jun1, 2, HUANG Jiajun1, 2, YANG Xinyi1, 2, HU Jiaqi1, 2
(1. Shenzhen Urban Planning & Land Resource Center, Shenzhen Guangdong 518040, China; 2. Guangdong Urban Planning & Traffic Simulation Decision Engineering Technology Research Center, Shenzhen Guangdong 518040, China; 3. School of Transportation, Southeast University, Nanjing Jiangsu 211189, China)

Abstract： The Bay Area is a key economic growth hub in China. Obtaining accurate cross-border transportation characteristics and patterns is crucial for measuring factors such as economic development, population mobility, and goods movement. This paper presents a review of the domestic and international development history of cross-border transportation surveys and systematically explores the theoretical foundations of cross-border transportation surveys in major cities within the Bay Area. The paper summarizes the urban planning and management needs for cross- border transportation surveys and establishes a survey framework guided by experience evaluation and new technologies and methods evaluation of big data. Specific survey methods are proposed in the framework, including sample size design, data verification, data expansion, and big data integration. Finally, using examples such as the spatial distribution of cross-border passengers between Shenzhen and Hong Kong, the distribution of connecting modes on the Shenzhen side, and the spatial distribution of passengers at Shenzhen Airport based on mobile signaling and survey data, the paper verifies the effectiveness of cross-border transportation survey methods and the feasibility of improving indicator accuracy. The method provides valuable insights for cross-border transportation surveys in regions such as the Bay Area, urban agglomerations, and metropolitan areas.

Keywords： transportation planning; cross- border transportation surveys; sampling techniques; big data methods; Bay Area; Shenzhen