Acta Geographica Sinica ›› 2020, Vol. 75 ›› Issue (4): 820-832.doi: 10.11821/dlxb202004011

• Transportation and Cultural Tourism • Previous Articles     Next Articles

Spatial pattern of intercity less-than-truckload logistics networks in China

REN Mengyao1,2, XIAO Zuopeng1,3(), James J. WANG4   

  1. 1. Harbin Institute of Technology, Shenzhen 518055, Guangdong, China
    2. The Hong Kong Polytechnic University, HKSAR 999077, China
    3. Shenzhen Key Laboratory of Urban Planning and Decision Making, Shenzhen 518055, Guangdong, China
    4. City University of Hong Kong, HKSAR 999077, China
  • Received:2018-08-27 Revised:2019-12-02 Online:2020-04-25 Published:2020-06-25
  • Contact: XIAO Zuopeng
  • Supported by:
    Major Program of National Natural Science Foundation of China(71390335);National Natural Science Foundation of China(41801151);Natural Science Foundation of Guangdong Province, China(2018A030310691)


Freight logistics is one of the most important socioeconomic connection elements between regions. The intercity logistics connection is an important perspective to understand regional production-consumption relationship and urban spatial structure. However, due to the lack of data and methods, few studies can effectively reveal the intercity logistics network pattern at the nation level. With the development and application of Internet and data science, the logistics resources and data integrated by the fourth party logistics platform provide new data source and research foundation for logistics studies. Drawing online less-than-truckload logistics transaction data for 3 months from November 2017 to January 2018, from the largest 4th party logistics (4PL) platform in China, this research aims to explore the intercity logistics connection pattern and urban logistics function differences. The outbound and inbound transaction numbers were calculated first for each prefecture-level city. With the approach of social network analysis (SNA), the logistics connection intensity and corresponding centrality were then computed and visualized for each city in the network. The research results show that: (1) the intercity logistics landscape roughly coincides with interregional socio-economic development. Taking three metropolises (i.e., Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta) as core triangle corridors, the national logistics connection pattern decreases from coastal area to inland areas. (2) Logistics activities are characterized by significant cross-regional flow. Active are intercity logistics shipment activities within the threshold of 1200 km distance. (3) According to the less-than-truckload logistics (LTL) network pattern, cities in China could be further divided into five categories, namely the national primary logistics city, national logistics center cities, national logistics node cities, regional logistics center cities and local logistics cities. These conclusions are expected to, from the perspective of intercity logistics connections, support national, provincial and city governments to make spatial planning and logistics planning.

Key words: intercity logistics connection, less-than-truckload logistics, social network analysis, spatial pattern, functional classification