Transportation Scenario Planning with Graph Neural Networks
This work addresses urban planning challenges for cities by providing a tool to simulate commuting flow changes, though it is incremental as it applies an existing model to new scenarios.
The paper tackled the problem of evaluating hypothetical changes in commuting flows for urban planning by leveraging a graph neural network model to assess different land use and infrastructure scenarios, validated through real-world case studies in two large Brazilian cities.
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.