# Optimal transport on large networks, a practitioner's guide

## TL;DR

This paper introduces a linear programming-based framework for modeling spatial allocation and pricing on large networks, with practical applications demonstrated through real-world examples and supporting tools for implementation.

## Contribution

It provides a novel linear programming formulation for equilibrium in network-based spatial markets, enabling analysis of large-scale data with modern solvers.

## Key findings

- Effective modeling of demand and pricing on large networks.
- Application to real-world examples like petrol stations and maternity wards.
- Availability of an R toolbox and online tutorial for practitioners.

## Abstract

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. 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. In addition to this paper we released a R toolbox to implement our results and an online tutorial (http://optimalnetwork.github.io)