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Commuting Network Models: Getting the Essentials

Author

Listed:
  • Floriana Gargiulo
  • Maxime Lenormand
  • Sylvie Huet
  • Omar Baqueiro Espinosa

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

  • Floriana Gargiulo & Maxime Lenormand & Sylvie Huet & Omar Baqueiro Espinosa, 2012. "Commuting Network Models: Getting the Essentials," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 15(2), pages 1-6.
    • Handle: RePEc:jas:jasssj:2011-12-3
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    References listed on IDEAS

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    1. Choukroun, Jean-Marc, 1975. "A general framework for the development of gravity-type trip distribution models," Regional Science and Urban Economics, Elsevier, vol. 5(2), pages 177-202, May.
    2. Roberto Patuelli & Aura Reggiani & Sean Gorman & Peter Nijkamp & Franz-Josef Bade, 2007. "Network Analysis of Commuting Flows: A Comparative Static Approach to German Data," Networks and Spatial Economics, Springer, vol. 7(4), pages 315-331, December.
    3. De Montis, Andrea & Chessa, Alessandro & Campagna, Michele & Caschili, Simone & Deplano, Giancarlo, 2010. "Modeling commuting systems through a complex network analysis: A study of the Italian islands of Sardinia and Sicily," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 2(3), pages 39-55.
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    Cited by:

    1. Lenormand, Maxime & Huet, Sylvie & Gargiulo, Floriana, 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. Maxime Lenormand & Sylvie Huet & Floriana Gargiulo & Guillaume Deffuant, 2012. "A Universal Model of Commuting Networks," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-7, October.
    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. 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.
    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.
    6. Lenormand, Maxime & Bassolas, Aleix & Ramasco, José J., 2016. "Systematic comparison of trip distribution laws and models," Journal of Transport Geography, Elsevier, vol. 51(C), pages 158-169.

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