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


  • Qinyu Wang Jiangsu Province Collaborative Innovation Centre of Modern Urban Traffic Technologies, School of Transportation, Southeast University
  • Weijie Yu Jiangsu Province Collaborative Innovation Centre of Modern Urban Traffic Technologies, School of Transportation, Southeast University
  • Wei Wang Jiangsu Province Collaborative Innovation Centre of Modern Urban Traffic Technologies, School of Transportation, Southeast University
  • Xuedong Hua Jiangsu Province Collaborative Innovation Centre of Modern Urban Traffic Technologies, School of Transportation, Southeast University



intercity mobility, spatial interaction gravity model, inverse gravity model, determination approach


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.


Xiang Y, et al. Investigating dominant trip distance for intercity passenger transport mode using large-scale location-based service data. Sustainability. 2019;11(19):5325. DOI:10.3390/su11195325.

Xian Y, et al. A two-phase approach for predicting highway passenger volume. Applied Sciences. 2021;11(14):6248. DOI: 10.3390/app11146248.

Zhu R, et al. Social sensing of the imbalance of urban and regional development in China through the population migration network around spring festival. Sustainability. 2020;12(8):3457. DOI: 10.3390/su12083457.

Shen G. Reverse-fitting the gravity model to inter-city airline passenger flows by an algebraic simplification. Journal of Transport Geography. 2004;12(3):219-234. DOI: 10.1016/j.jtrangeo.2003.12.006.

Zhu M, Sun Z. Analysis of Chinese urban attraction based on user network query behavior data [基于用户网络查询行为数据的中国城市吸引力分析]. Urban Development Studies [城市发展研究]. 2019;26(10):115-124. DOI: 10.3969/j.issn.1006-3862.2019.10.022.

Shen J, Lu Y. Evaluation about composite attraction index of tourism in Chinese cities [中国市域旅游综合吸引力指数评价]. Journal of Natural Resources [自然资源学报]. 2012;27(4):661-673. DOI: 10.11849/zrzyxb.2012.04.012.

Zhang H, Leung X, Bai B. Cultural attractiveness index for sustainable cities: Tourism Agenda 2030. Tourism Review. 2023;78:2. DOI: 10.1108/TR-05-2022-0255.

Wang S, Wang J, Liu X. How do urban spatial structures evolution in the high-speed rail era? Case study of Yangtze River Delta, China. Habitat International. 2019;93:102051. DOI: 10.1016/j.habitatint.2019.102051.

Xiao Y, Wang F, Liu Y, Wang J. Reconstructing gravitational attractions of major cities in China from air passenger flow data, 2001–2008: A particle swarm optimization approach. The Professional Geographer. 2013;65(2):265-282. DOI: 10.1080/00330124.2012.679445.

Zhang Y, Li X(Robert), Cárdenas DA, Liu Y. Calculating theme parks’ tourism demand and attractiveness energy: An inverse gravity model and particle swarm optimization. Journal of Travel Research. 2022;61(2):314-330. DOI: 10.1177/0047287520977705.

He Z, Wu B, Liu Y. Study of spatial interaction and nodal attractions of municipal cities in China from social media check-in data. Acta Scientiarum Naturalium Universitatis Pekinensis. 2017;53(5):862-872. DOI: 10.13209/j.0479-8023.2017.084.

Ma H, Xu X. High-quality development assessment and spatial heterogeneity of urban agglomeration in the Yellow River Basin [黄河流域城市群高质量发展评估与空间格局分异]. Economic Geography [经济地理]. 2020;40(4):11-18. DOI: 10.15957/j.cnki.jjdl.2020.04.002.

Yan G, Zhang Y. Coupling coordination degree of the urban population flow tendency strength and urban gravity in northeast China based on network attention data. Scientia Geographica Sinica. 2020;40(11):1848-1858. DOI: 10.13249/j.cnki.sgs.2020.11.010.

Jin F, Liu H, Xu X. Nodal attractions estimation and their influencing factors based on reverse gravity mode. Progress in Geography. 2011;30(4):485-490. DOI: 10.11820/dlkxjz.2011.04.012.

Khadaroo J, Seetanah B. The role of transport infrastructure in international tourismdevelopment: A gravity model approach. Tourism Management. 2008;29(5):831-840. DOI: 10.1016/j.tourman.2007.09.005.

Fofanova KV, Sychev AA. Factors in migration attractiveness of a provincial city: The case study of the city of Saransk. Regionology. Russian Journal of Regional Studies. 2019;27(4):756-778. DOI: 10.15507/2413-1407.109.027.201904.756-778.

He J, et al. Measuring urban spatial interaction in Wuhan Urban Agglomeration, Central China: A spatially explicit approach. Sustainable Cities and Society, 2017;32:569-583. DOI: 10.1016/j.scs.2017.04.014.

Marrocu E, Paci R. Different tourists to different destinations. Evidence from spatial interaction models. Tourism Management. 2013;39:71-83. DOI: 10.1016/j.tourman.2012.10.009.

Gao Y, et al. Extracting spatial patterns of intercity tourist movements from online travel blogs. Sustainability. 2019;11(13):3526. DOI: 10.3390/su11133526.

Liu Y, Sui Z, Kang C, Gao Y. Uncovering patterns of inter-urban trip and spatial interaction from social media check-in data. PloS One. 2014;9(1):e86026. DOI: 10.1371/journal.pone.0086026.

Surya B, Ahmad DNA, Sakti HH, Sahban H. Land use change, spatial interaction, and sustainable development in the metropolitan urban areas, South Sulawesi Province, Indonesia. Land. 2020;9(3):95. DOI: 10.3390/land9030095.

Xia N, et al. Accessibility based on Gravity-Radiation model and Google Maps API: A case study in Australia. Journal of Transport Geography. 2018;72:178-190. DOI: 10.1016/j.jtrangeo.2018.09.009.

Yu W, Wang W, Hua X, Wei X. Exploring taxi demand distribution of comprehensive land-use patterns using online car-hailing data and points of interest in Chengdu, China. Transportation Research Record. 2021;2675(10):1268-1286. DOI: 10.1177/03611981211015259.

Yang B, et al. How to improve urban transportation planning in big data era? A practice in the study of traffic analysis zone delineation. Transport Policy. 2022;127:1-14. DOI: 10.1016/j.tranpol.2022.08.002.

Yu W, Wang W, Hua X, Miao D. Exploring free floating bike sharing travel patterns using travel records and online point of interests. CICTP 2020. 2020. p. 2758-2769.

Shi F, Zhu L. Analysis of trip generation rates in residential commuting based on mobile phone signaling data. Journal of Transport and Land Use. 2019;12(1):201-220. DOI: 10.5198/jtlu.2019.1431.

Evans SP. A relationship between the gravity model for trip distribution and the transportation problem in linear programming. Transportation Research. 1973;7(1):39-61. DOI: 10.1016/0041-1647(73)90005-1.

Li R, et al. Gravity model in dockless bike-sharing systems within cities. Physical Review E. 2021;103(1):01231. DOI: 10.1103/PhysRevE.103.012312.

Menard S. Applied logistic regression analysis. Newcastle upon Tyne, UK: SAGE Publications; 2001.

James G, Witten D, Hastie T, Tibshirani R. An introduction to statistical learning. New York, USA: Springer; 2013. [Accessed 15th August 2022].

Marquaridt DW. Generalized inverses, ridge regression, biased linear estimation, and nonlinear estimation. Technometrics. 1970;12(3):591-612. DOI: 10.1080/00401706.1970.10488699.

Vittinghoff E, Glidden DV, Shiboski SC, McCulloch CE. Regression methods in biostatistics: Linear, logistic, survival, and repeated measures models. New York, USA: Springer; 2006.

Ismael K, Duleba S. An integrated ordered probit model for evaluating university commuters’ satisfaction with public transport. Urban Science. 2023;7(3):83. DOI: 10.3390/urbansci7030083.




How to Cite

Wang, Q., Yu, W., Wang, W., & Hua, X. (2024). Towards Intercity Mobility System – Insights into the Spatial Interaction Gravity Model and Determination Approach. Promet - Traffic&Transportation, 36(2), 326–344.