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A commuting generation model requiring only aggregated data

Abstract : We recently proposed, in (Gargiulo et al., 2011), an innovative stochastic model with only one parameter to calibrate. It reproduces the complete network by an iterative process stochastically choosing, for each commuter living in the municipality of a region, a workplace in the region. The choice is done considering the job offer in each municipality of the region and the distance to all the possible destinations. The model is quite effective if the region is sufficiently autonomous in terms of job offers. However, calibrating or being sure of this autonomy require data or expertise which are not necessarily available. Moreover the region can be not autonomous. In the present, we overcome these limitations, extending the job search geographical base of the commuters to the outside of the region, and changing the deterrence function form. We also found a law to calibrate the improvement model which does not require data.
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Submitted on : Monday, July 6, 2020 - 1:46:46 PM
Last modification on : Wednesday, September 28, 2022 - 3:08:41 PM
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  • HAL Id : hal-02596121, version 1
  • IRSTEA : PUB00033826



Maxime Lenormand, Guillaume Deffuant, S. Huet. A commuting generation model requiring only aggregated data. 7th European Social Simulation Association Conference, Sep 2011, Montpellier, France. pp.16. ⟨hal-02596121⟩



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