Redai dili (Nov 2022)
Urban Network Structures and Organization Models Based on the Road Less-Truck-Load Dedicated Line Data in China
Abstract
Against the background of the increasing trend of fragmentation of freight demand, the spatial structure analysis of urban networks using road Less-Truck-Load (LTL) dedicated lines has positive implications for enriching the flow space theory and empirical evidence. Based on the social network analysis method, this study used the data of the national prefecture-level and above cities' road LTL dedicated lines on the China Communications Logistics LOGINK System in 2018, and conducted feature mining of Chinese city network relationships from the perspective of road LTL dedicated lines at three levels: city nodes, intercity connections, and sub-networks. The results show the following: (1) Shanghai, Tianjin, Zhengzhou, Guangzhou, and Hangzhou dominated the network. Based on the cargo flow organization coefficients, urban nodes can be divided into four types: strong center, second strong center, weak center, and subordinate. The number of high-grade cities in the network was relatively small and mainly concentrated in the eastern and central regions. The imbalance in the spatial distribution is obvious. (2) Among the top ten cities in terms of the amount of first contact, the ratio of export-oriented cities to import-oriented cities is 4:1, which reflects the imbalance in cargo flow. The network space carved by the road LTL dedicated lines data showed a significant distance attenuation law. The road LTL dedicated lines connections are mainly distributed in the intercity range of 0-200 km and the interprovincial range of 200-500 km, with the number of special lines concentrated in 500 km accounting for 41.9%. (3) The community detection algorithm was used to identify six urban communities with significant regional characteristics, including the Northeast Jilumeng, Zhongyuan, Guanzhong, Jianghuai, Pan-Pearl, Delta-Yangtze River Delta, and Changzhutan communities. The community structures showed clear spatial agglomeration and cross-administrative features. (4) To enhance the status of the nodes of the road LTL network and optimize the organization of the network space, the following suggestions are put forward: Urban networks based on the road LTL dedicated line data should enhance the service capacity of the Chengdu-Chongqing urban agglomeration road LDL line, strengthen the industrial agglomeration and driving role of core cities, optimize the industrial structure of marginal cities, strengthen the integration of transportation and industry, and actively guide the car-free carrier platform to improve the efficiency of road freight organization. In future studies, long-term cycles and multiple data sources should be enhanced to verify the validity and reliability of the findings.
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