Generating Origin-Destination Matrices in Neural Spatial Interaction Models

• URL: http://arxiv.org/abs/2410.07352v1
• Date: Wed, 9 Oct 2024 18:09:02 GMT
• Title: Generating Origin-Destination Matrices in Neural Spatial Interaction Models
• Authors: Ioannis Zachos, Mark Girolami, Theodoros Damoulas,
• Abstract summary: Agent-based models (ABMs) are proliferating as decision-making tools across policy areas in transportation, economics, and epidemiology. A central object of interest is the discrete origin-destination matrix which captures interactions and agent trip counts between locations. Existing approaches resort to continuous approximations of this matrix and subsequent ad-hoc discretisations in order to perform ABM simulation and calibration. This impedes conditioning on partially observed summary statistics, fails to explore the multimodal matrix distribution over a discrete support, and incurs discretisation errors.
• Score: 11.188781092933313
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Agent-based models (ABMs) are proliferating as decision-making tools across policy areas in transportation, economics, and epidemiology. In these models, a central object of interest is the discrete origin-destination matrix which captures spatial interactions and agent trip counts between locations. Existing approaches resort to continuous approximations of this matrix and subsequent ad-hoc discretisations in order to perform ABM simulation and calibration. This impedes conditioning on partially observed summary statistics, fails to explore the multimodal matrix distribution over a discrete combinatorial support, and incurs discretisation errors. To address these challenges, we introduce a computationally efficient framework that scales linearly with the number of origin-destination pairs, operates directly on the discrete combinatorial space, and learns the agents' trip intensity through a neural differential equation that embeds spatial interactions. Our approach outperforms the prior art in terms of reconstruction error and ground truth matrix coverage, at a fraction of the computational cost. We demonstrate these benefits in large-scale spatial mobility ABMs in Cambridge, UK and Washington, DC, USA.

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