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

TL;DR

This survey integrates theory and practice across disciplines to understand origin-destination flows, presenting gravity, intervening opportunities, and radiation models as foundational theories while detailing practical tasks—prediction, construction, estimation, and forecasting—supported by traditional, ML, and deep learning methods. It highlights mobility and auxiliary data sources, data integration challenges, and the roles of OD flows in urban planning, traffic management, and epidemic control. The work emphasizes the need for theory-informed, data-driven hybrids and urban-simulation coupling to address scalability, explainability, and cross-city transfer. Collectively, it offers a comprehensive blueprint for interdisciplinary research and practical deployment of OD-flow models in modern urban systems.

Abstract

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.

Figures (6)

• Figure 1: Research on origin-destination flows across different domains. The figure illustrates four classical practical issues related to OD flows, each of which possesses strong interdisciplinary attributes. To explicitly showcase the contribution of each discipline to these problems, we employ a radar chart to interpret the contribution degree of each discipline to the respective issue.
• Figure 2: A timeline of important research on origin-destination flows in diverse academic disciplines.
• Figure 3: A schematic diagram of commonly used mobility data related to OD flows.
• Figure 4: Relationship among the gravity model, intervening opportunities model, and radiation model.
• Figure 5: The illustrations of comparing different model-based generation methods for solving OD construction problem.

Theorems & Definitions (4)

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