Towards Intercity Mobility System – Insights into the Spatial Interaction Gravity Model and Determination Approach

• Published in: Promet Vol. 36; no. 2; pp. 326 - 344
• Main Authors: Wang, Qinyu, Yu, Weijie, Wang, Wei, Hua, Xuedong
• Format: Journal Article Paper
• Language: English
• Published: Sveučilište u Zagrebu Fakultet prometnih znanosti 30.04.2024; University of Zagreb, Faculty of Transport and Traffic Sciences
• Subjects: determination approach; intercity mobility; inverse gravity model; spatial interaction gravity model
• ISSN: 0353-5320; 1848-4069
• DOI: 10.7307/ptt.v36i2.414

The current development of urban agglomeration greatly promotes the intercity connection and elevates the significance of intercity mobility system. However, intercity mobility often exhibits extreme spatiotemporal imbalances due to the diverse urban characteristics. This poses a huge challenge for traffic management and reveals the necessity on understanding the urban attractiveness for intercity mobility, which is represented as spatial interaction gravity in this study. While recent works have explored relevant aspects, they failed to provide insights into temporal variations in spatial interaction gravity or capture the determining factors from multiple perspectives. To fill this gap, this study proposed a two-phase framework to measure the urban spatial interaction gravity and developed determination approaches utilising the large-scale location-based services (LBS) dataset. Specifically, the inverse gravity model was adopted for the measure within multiple urban agglomerations and city sets during weekdays, weekends and holidays. Then, we developed the fitting equations of spatial interaction gravity by incorporating the correlated features associated with social, economic, network accessibility and land use. The findings present spatial interaction gravity across different periods and substantiate the distinct determination effects of features, with a high fitting accuracy. They provide promising supports for the intercity mobility prediction and pre-emptive traffic management.