Commuting Network Models: Getting the Essentials

被引:23
作者
Gargiulo, Floriana
Lenormand, Maxime
Huet, Sylvie
Espinosa, Omar Baqueiro
机构
[1] Paris
[2] BP50085, Aubire cedex 75020
[3] LISC lab of CEMAGREF, Clermont Ferrand
[4] INED, Paris
[5] Aubiere- Clermont Ferrand 63172, 24 avenue de Landais
[6] Cemagref-LISC, F-63172 Aubière Cedex, 24 avenue des Landais
[7] Leibniz Institute for Agricultural Development in Central and Eastern Europe (IAMO), Halle 06120
来源
JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION | 2012年 / 15卷 / 02期
关键词
Commuting Patterns; Network Generation Models; Individual Based Models; Stochastic Models; DISTANCE-DECAY; FLOWS;
D O I
10.18564/jasss.1964
中图分类号
C [社会科学总论];
学科分类号
03 ; 0303 ;
摘要
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.
引用
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页数:17
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