Quantitative spatiotemporal evolution of port-hinterland connection using truck trajectory data: case analysis

ABSTRACT

This study proposes a new framework for evaluating the spatiotemporal characteristics of port-related hinterlands and their connection with seaports using truck trajectory data. The framework was applied to a case study of the Port of Rizhao in Shandong province, China. The ‘STOP-MOVE’ model and density-based spatial clustering of applications analysis were used to identify truck stopping points, which were found to be closely related to the activity of the export-oriented economic condition. The number of trucks serving factories showed a strong correlation with the GDP of cities, with a Pearson correlation index of 0.923. The main hinterland with a solid connection to the Port of Rizhao is found to be located in the southern region of Shandong province, such as the cities of Linyi and Jining. This connection is influenced by the disruption of working days due to the Chinese New Year holidays and the convenience of logistics determined by the local economic structure. The strong port-hinterland connection at the provincial and municipal levels is found to be weak at the county level. This framework can potentially be used to evaluate port-hinterland connections in other regions and deepen understanding of hinterland cognition.

KEYWORDS:

• Hinterland
• convenience of logistics
• spatial computing
• trajectories of trucks

Data availability statement

The datasets generated analysed during the current study are available from the corresponding author on reasonable request.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Natural Science Foundation of China: [Grant Number 71804059]; Natural Science Foundation of Fujian Province: [Grant Number 2021J01821]; Shanghai Science and Technology Committee: [Grant Number 18DZ1206300]; the National Key Research and Development Program of China: [Grant Number 2018YFC1407400].