Activity-travel pattern inference based on multi-source big data

IF 9.5 1区 工程技术 Q1 TRANSPORTATION
Xiao Fu , Yi Zhang , Juan de Dios Ortúzar , Guonian Lü
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引用次数: 0

Abstract

We provide a comprehensive review of the literature on inferring activity-travel patterns (ATP) using multi-source big data; the increasing number of publications over time on this subject, demonstrates the importance of big data in this task. Our aims are to identify the advantages and research gaps in ATP inference and to promote further developments in this field. We clarify the fundamental concepts (i.e. ATP and its components), commonly used data sources, and inference processes employed in ATP inference studies. Emphasis is placed on two prominent big data sources: mobile phone data and smart card data. We outline the various approaches involved in the inference process, and we highlight existing shortcomings in data sources, ATP inference methodologies, and result validation. Based on the review, it is evident that future research should address several limitations in ATP inference. Firstly, it is necessary to improve the comprehensive understanding of ATP and understand the interrelationships among its different components. Secondly, it is necessary to integrate different data sources and leverage their respective strengths to gain deeper insights into activity-travel behaviour. Lastly, further investigation into emerging technologies such as artificial intelligence in ATP inference is warranted to improve inference accuracy. The findings of this study could provide valuable insights for policy makers, enabling them to gain a deeper understanding of activity-travel choice behaviour and develop more effective policies related to transportation system.
基于多源大数据的活动-出行模式推断
我们对利用多源大数据推断活动-旅行模式(ATP)的文献进行了全面综述;随着时间的推移,关于这一主题的出版物越来越多,这表明了大数据在这一任务中的重要性。我们的目标是确定ATP推理的优势和研究差距,并促进该领域的进一步发展。我们澄清了基本概念(即ATP及其成分),常用的数据源和ATP推理研究中使用的推理过程。重点介绍两个突出的大数据源:手机数据和智能卡数据。我们概述了推理过程中涉及的各种方法,并强调了数据源、ATP推理方法和结果验证方面存在的缺点。基于回顾,很明显,未来的研究应该解决ATP推断的几个限制。首先,需要提高对ATP的全面认识,了解其不同组分之间的相互关系。其次,有必要整合不同的数据源,发挥各自的优势,以更深入地了解活动-旅行行为。最后,需要进一步研究人工智能等新兴技术,以提高ATP推理的准确性。研究结果可以为政策制定者提供有价值的见解,使他们能够更深入地了解活动-旅行选择行为,并制定更有效的交通系统相关政策。
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来源期刊
Transport Reviews
Transport Reviews TRANSPORTATION-
CiteScore
17.70
自引率
1.00%
发文量
32
期刊介绍: Transport Reviews is an international journal that comprehensively covers all aspects of transportation. It offers authoritative and current research-based reviews on transportation-related topics, catering to a knowledgeable audience while also being accessible to a wide readership. Encouraging submissions from diverse disciplinary perspectives such as economics and engineering, as well as various subject areas like social issues and the environment, Transport Reviews welcomes contributions employing different methodological approaches, including modeling, qualitative methods, or mixed-methods. The reviews typically introduce new methodologies, analyses, innovative viewpoints, and original data, although they are not limited to research-based content.
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