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

TL;DR

This survey comprehensively reviews interdisciplinary approaches to OD flow modeling, covering theoretical models, practical problems, and computational techniques, aiming to foster cross-disciplinary collaboration and innovation.

Contribution

It provides a holistic overview of OD flow research across disciplines, highlighting key models, practical challenges, and future directions to promote interdisciplinary understanding.

Findings

Identified three influential theoretical models: gravity, intervening opportunities, and radiation.

Highlighted four practical problems: prediction, construction, estimation, and forecasting.

Emphasized the integration of advanced computational models like deep learning.

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.