Optimal transport on large networks

With Alfred Galichon and Lucas Vernet, we recently uploaded a paper entitled optimal transport on large networks on arxiv.

This article presents a set of tools for the modeling of a spatial allocation problem in a large geographic market and gives examples of applications. In our settings, the market is described by a network that maps the cost of travel between each pair of adjacent locations. Two types of agents are located at the nodes of this network. The buyers choose the most competitive sellers depending on their prices and the cost to reach them. Their utility is assumed additive in both these quantities. Each seller, taking as given other sellers prices, sets her own price to have a demand equal to the one we observed. We give a linear programming formulation for the equilibrium conditions. After formally introducing our model we apply it on two examples: prices offered by petrol stations and quality of services provided by maternity wards (only the later is described here for privacy issues). These examples illustrate the applicability of our model to aggregate demand, rank prices and estimate cost structure over the network. We insist on the possibility of applications to large scale data sets using modern linear programming solvers such as Gurobi.

Demand for gas in gas stations in Britanny, and demand for maternity in France (with border correction)

In addition to this paper we released a R toolbox to implement our results and an online tutorial, optimalnetwork.github.io.

Cite this blog post
Arthur Charpentier (2019, July 4). Optimal transport on large networks. Freakonometrics. Retrieved April 18, 2024, from https://doi.org/10.58079/ovdh

5 thoughts on “Optimal transport on large networks”

  1. Excellent, I have learned a lot from this artical.
    Would you share us data sets in this artical such as .csv and .rds files, thank you very much, hopfully to see you next research.

  2. Thank you for this tutorial. Could you please give direct link to download open datasets used in you work in order to reproduce the results.

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