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Commuting network models: Getting the essentials

Author

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  • Gargiulo, Floriana
  • Lenormand, Maxime
  • Huet, Sylvie
  • Baqueiro Espinosa, Omar

Abstract

Human mobility and, in particular, commuting patterns have a fundamental role in understanding socio-economic systems. Analysing and modelling the networks formed by commuters, for example, has become a crucial requirement in studying rural areas dynamics and to help decision-making. This paper presents a simple spatial interaction commuting model with only one parameter. The proposed algorithm considers each individual who wants to commute, starting from their residence to all the possible workplaces. The algorithm decides the location of the workplace following the classical rule inspired from the gravity law consisting of a compromise between the job offers and the distance to the job. The further away the job is, the more important the offer should be to be considered for the decision. Inversely, the quantity of offers is not important for the decision when these offers are close by. The presented model provides a simple, yet powerful approach to simulate realistic distributions of commuters for empirical studies with limited data availability. The paper also presents a comparative analysis of the structure of the commuting networks of the four European regions to which we apply our model. The model is calibrated and validated on these regions. The results from the analysis show that the model is very efficient in reproducing most of the statistical properties of the network given by the data sources.

Suggested Citation

  • Gargiulo, Floriana & Lenormand, Maxime & Huet, Sylvie & Baqueiro Espinosa, Omar, 2012. "Commuting network models: Getting the essentials," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 15(2).
    • Handle: RePEc:zbw:espost:230636
    DOI: 10.18564/jasss.1964
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    References listed on IDEAS

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    1. van den Berg, Gerard J & Gorter, Cees, 1997. "Job Search and Commuting Time," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(2), pages 269-281, April.
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    1. Maxime Lenormand & Sylvie Huet & Floriana Gargiulo, 2014. "Generating French virtual commuting networks at the municipality level," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 7(1), pages 43-55.
    2. Constanza Fosco, 2012. "Spatial Difusion and Commuting Flows," Documentos de Trabajo en Economia y Ciencia Regional 30, Universidad Catolica del Norte, Chile, Department of Economics, revised Sep 2012.
    3. Rongrong Hong & Wenming Rao & Dong Zhou & Chengchuan An & Zhenbo Lu & Jingxin Xia, 2020. "Commuting Pattern Recognition Using a Systematic Cluster Framework," Sustainability, MDPI, vol. 12(5), pages 1-20, February.
    4. Andrew Smith & Robin Lovelace & Mark Birkin, 2017. "Population Synthesis with Quasirandom Integer Sampling," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(4), pages 1-14.
    5. Thompson, C.A. & Saxberg, K. & Lega, J. & Tong, D. & Brown, H.E., 2019. "A cumulative gravity model for inter-urban spatial interaction at different scales," Journal of Transport Geography, Elsevier, vol. 79(C), pages 1-1.

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