Transportation Scenario Planning with Graph Neural Networks

TL;DR

This paper introduces a method using graph neural networks to evaluate how urban land use and infrastructure changes impact commuting flows, aiding city planning.

Contribution

It applies the GMEL graph neural network model to urban transportation scenario planning, demonstrating its effectiveness with real-world case studies.

Findings

Effective modeling of commuting flow changes

Validation on two large Brazilian cities

Potential for urban planning applications

Abstract

Providing efficient human mobility services and infrastructure is one of the major concerns of most mid-sized to large cities around the world. A proper understanding of the dynamics of commuting flows is, therefore, a requisite to better plan urban areas. In this context, an important task is to study hypothetical scenarios in which possible future changes are evaluated. For instance, how the increase in residential units or transportation modes in a neighborhood will change the commuting flows to or from that region? In this paper, we propose to leverage GMEL, a recently introduced graph neural network model, to evaluate changes in commuting flows taking into account different land use and infrastructure scenarios. We validate the usefulness of our methodology through real-world case studies set in two large cities in Brazil.