# Generating Origin-Destination Matrices in Neural Spatial Interaction Models

arXiv: https://arxiv.org/abs/2410.07352 · HTML: https://mlsanity.com/paper/2410.07352/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 48 references that are in the map.
- First posted on arXiv 9 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [A General Framework for Updating Belief Distributions](https://mlsanity.com/paper/1306.6430/) (2013)
- [PyTorch Distributed: Experiences on Accelerating Data Parallel Training](https://mlsanity.com/paper/2006.15704/) (2020)
- [Learning from Aggregate Observations](https://mlsanity.com/paper/2004.06316/) (2020)
- [Learning Geo-Contextual Embeddings for Commuting Flow Prediction](https://mlsanity.com/paper/2005.01690/) (2020)
- [A Machine Learning Approach to Modeling Human Migration](https://mlsanity.com/paper/1711.05462/) (2017)
- [Neural parameter calibration for large-scale multi-agent models](https://mlsanity.com/paper/2209.13565/) (2022)
- [Reconstructing commuters network using machine learning and urban indicators](https://mlsanity.com/paper/1908.03512/) (2019)
- [Accurate Calibration of Agent-based Epidemiological Models with Neural Network Surrogates](https://mlsanity.com/paper/2010.06558/) (2020)
- [Stochastic Modelling of Urban Structure](https://mlsanity.com/paper/1805.03567/) (2018)
- [Table inference for combinatorial origin-destination choices in agent-based population synthesis](https://mlsanity.com/paper/2307.02184/) (2023)

## Built on by (most cited first, 1 of 1)

- [Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based Inference](https://mlsanity.com/paper/2412.05590/) (2024)

## Abstract (arXiv)

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.