@misc{zachos2024a,
  title = {Generating {{Origin-Destination Matrices}} in {{Neural Spatial Interaction Models}}},
  author = {Zachos, Ioannis and Girolami, Mark and Damoulas, Theodoros},
  year = {2024},
  month = oct,
  number = {arXiv:2410.07352},
  eprint = {2410.07352},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2410.07352},
  urldate = {2024-10-11},
  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.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Machine Learning,Statistics - Machine Learning}
}
