起点-终点流动模型的跨学科研究:理论与技术

Can Rong, Jingtao Ding, Yong Li
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摘要

起点-目的地流模型是一个广泛研究的跨学科课题,如交通运输中的出行需求研究和地理学中的空间相互作用模型。然而,不同领域的研究人员往往采用自己独特的研究范式,缺乏跨学科的交流,阻碍了知识的交叉施肥和挑战的新解决方案的发展。本文提出了一个系统的跨学科调查,从利用基础理论到研究人口流动机制,再到用计算模型等工程技术解决实际问题,全面、整体地审视人口流动。具体而言,区域经济学、城市地理学和社会物理学擅长运用理论研究方法来探索OD流动的潜在机制。他们发展了三种有影响力的理论模型:引力模型、介入机会模型和辐射模型。这些模型分别着重于考察距离、机会和人口对OD流动的基本影响。与此同时,交通、城市规划和计算机科学等领域主要关注四个实际问题:OD预测、OD构建、OD估计和OD预测。先进的计算模型,如深度学习模型,已经逐渐被引入来更有效地解决这些问题。最后,在现有研究的基础上,本调查总结了当前面临的挑战,并概述了本课题的未来发展方向。通过这次调查,我们的目标是打破学科之间的障碍,在与流动相关的研究,培养跨学科的观点和思维模式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Interdisciplinary Survey on Origin-destination Flows Modeling: Theory and Techniques
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.
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