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

• Published in: ACM computing surveys Vol. 57; no. 1; pp. 1 - 49
• Main Authors: Rong, Can, Ding, Jingtao, Li, Yong
• Format: Journal Article
• Language: English
• Published: New York, NY ACM 01.01.2025; Association for Computing Machinery
• Subjects: Applied computing; Geography; Interaction models; Interdisciplinary aspects; Modelling; Regional development; Research methodology; Travel demand; Urban planning; Urban mobility; modeling; origin-destination flows; interdisciplines
• ISSN: 0360-0300; 1557-7341
• DOI: 10.1145/3682058

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. We have constructed the benchmarks for these four problems at https://github.com/tsinghua-fib-lab/OD_benckmark. 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.