Generating Origin-Destination Matrices in Neural Spatial Interaction Models
<|reference_start|>Generating Origin-Destination Matrices in Neural Spatial Interaction Models: 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.<|reference_end|>
@article{zachos2024generating,
    title={Generating Origin-Destination Matrices in Neural Spatial Interaction
  Models},
    author={Ioannis Zachos, Mark Girolami, Theodoros Damoulas},
    journal={arXiv preprint arXiv:2410.07352},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.07352},
    primaryClass={cs.LG stat.ML}
}