{"title":"An Interdisciplinary Survey on Origin-destination Flows Modeling: Theory and Techniques","authors":"Can Rong, Jingtao Ding, Yong Li","doi":"arxiv-2306.10048","DOIUrl":null,"url":null,"abstract":"Origin-destination~(OD) flow modeling is an extensively researched subject\nacross multiple disciplines, such as the investigation of travel demand in\ntransportation and spatial interaction modeling in geography. However,\nresearchers from different fields tend to employ their own unique research\nparadigms and lack interdisciplinary communication, preventing the\ncross-fertilization of knowledge and the development of novel solutions to\nchallenges. This article presents a systematic interdisciplinary survey that\ncomprehensively and holistically scrutinizes OD flows from utilizing\nfundamental theory to studying the mechanism of population mobility and solving\npractical problems with engineering techniques, such as computational models.\nSpecifically, regional economics, urban geography, and sociophysics are adept\nat employing theoretical research methods to explore the underlying mechanisms\nof OD flows. They have developed three influential theoretical models: the\ngravity model, the intervening opportunities model, and the radiation model.\nThese models specifically focus on examining the fundamental influences of\ndistance, opportunities, and population on OD flows, respectively. In the\nmeantime, fields such as transportation, urban planning, and computer science\nprimarily focus on addressing four practical problems: OD prediction, OD\nconstruction, OD estimation, and OD forecasting. Advanced computational models,\nsuch as deep learning models, have gradually been introduced to address these\nproblems more effectively. Finally, based on the existing research, this survey\nsummarizes current challenges and outlines future directions for this topic.\nThrough this survey, we aim to break down the barriers between disciplines in\nOD flow-related research, fostering interdisciplinary perspectives and modes of\nthinking.","PeriodicalId":501310,"journal":{"name":"arXiv - CS - Other Computer Science","volume":"28 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Other Computer Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2306.10048","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.