# An Interdisciplinary Survey on Origin-destination Flows Modeling: Theory and Techniques

arXiv: https://arxiv.org/abs/2306.10048 · HTML: https://mlsanity.com/paper/2306.10048/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2023.

## Built on by (most cited first, 13 of 13)

- [Urban Generative Intelligence (UGI): A Foundational Platform for Agents in Embodied City Environment](https://mlsanity.com/paper/2312.11813/) (2023)
- [TransitReID: Transit OD Data Collection with Occlusion-Resistant Dynamic Passenger Re-Identification](https://mlsanity.com/paper/2504.11500/) (2025)
- [Calibration of Vehicular Traffic Simulation Models by Local Optimization](https://mlsanity.com/paper/2502.11585/) (2025)
- [Satellites Reveal Mobility: A Commuting Origin-destination Flow Generator for Global Cities](https://mlsanity.com/paper/2505.15870/) (2025)
- [A Survey of Physics-Informed AI for Complex Urban Systems](https://mlsanity.com/paper/2506.13777/) (2025)
- [UMOD: A Novel and Effective Urban Metro Origin-Destination Flow Prediction Method](https://mlsanity.com/paper/2409.04942/) (2024)
- [Deep learning four decades of human migration](https://mlsanity.com/paper/2506.22821/) (2025)
- [VisitHGNN: Heterogeneous Graph Neural Networks for Modeling Point-of-Interest Visit Patterns](https://mlsanity.com/paper/2510.02702/) (2025)
- [Fusing Urban Structure and Semantics: A Conditional Diffusion Model for Cross-City OD Matrix Generation](https://mlsanity.com/paper/2605.00938/) (2026)
- [DynaOD: Dynamic Origin-Destination Flow Generation with Discrete-to-Continuous Temporal Semantic Modeling](https://mlsanity.com/paper/2606.09086/) (2026)
- [SalAngaBhava: A Sinhala Market Dataset for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2607.05259/) (2026)
- [EventOD: Event-Aware OD Flow Generation via LLM-Guided Semantic Modulation](https://mlsanity.com/paper/2607.22655/) (2026)
- [SeoulMMOD: A Large-Scale Multimodal Origin-Destination Flow Benchmark](https://mlsanity.com/paper/2610.13730/) (2026)

## Abstract (arXiv)

Origin-destination (OD) flow modeling is an extensively researched subject across multiple disciplines, such as the investigation of travel demand in transportation and spatial interaction modeling in geography. However, researchers from different fields tend to employ their own unique research paradigms and lack interdisciplinary communication, preventing the cross-fertilization of knowledge and the development of novel solutions to challenges. This article presents a systematic interdisciplinary survey that comprehensively and holistically scrutinizes OD flows from utilizing fundamental theory to studying the mechanism of population mobility and solving practical problems with engineering techniques, such as computational models. Specifically, regional economics, urban geography, and sociophysics are adept at employing theoretical research methods to explore the underlying mechanisms of OD flows. They have developed three influential theoretical models: the gravity model, the intervening opportunities model, and the radiation model. These models specifically focus on examining the fundamental influences of distance, opportunities, and population on OD flows, respectively. In the meantime, fields such as transportation, urban planning, and computer science primarily focus on addressing four practical problems: OD prediction, OD construction, OD estimation, and OD forecasting. Advanced computational models, such as deep learning models, have gradually been introduced to address these problems more effectively. Finally, based on the existing research, this survey summarizes current challenges and outlines future directions for this topic. Through this survey, we aim to break down the barriers between disciplines in OD flow-related research, fostering interdisciplinary perspectives and modes of thinking.