Exploring changes in residents' daily activity patterns through sequence visualization analysis

IF 2.3 4区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IET Intelligent Transport Systems Pub Date : 2024-10-17 DOI:10.1049/itr2.12511
Xiaoran Peng, Ruimin Hu, Xiaochen Wang, Nana Huang
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Abstract

The analysis of people's daily activities has played a crucial role in various applications, such as urban geography, activity prediction, and homogeneous population detection. However, limited studies have explored changes in the residents’ activity patterns in a particular region across various periods. To explore the changes, a methodological framework of sequence visualization analysis based on machine learning that extracts the activity patterns across various periods using sequence analysis, visualizes the activity patterns by calculating the frequency of different activities at time points and categorizes them through graphical similarity, and then compares the activity patterns in terms of activity and demographic characteristics is proposed. Empirical testing on the New York Metropolitan data of the National Household Travel Survey (NHTS) is conducted for 2001, 2009, and 2017. The findings reveal significant intra-similarities, inter-differences, and distinct changes in activity patterns across three periods for different social populations in the New York Metropolitan. From the perspective of information analysis, this work is anticipated to enhance the understanding of travel needs for diverse social populations in a particular region, thereby facilitating targeted policy adjustments for the departments concerned.

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通过序列可视化分析探索居民日常活动模式的变化
对人们日常活动的分析在城市地理、活动预测和同质人口检测等各种应用中发挥着至关重要的作用。然而,对特定地区居民活动模式在不同时期的变化进行探讨的研究却很有限。为了探索这些变化,本文提出了一种基于机器学习的序列可视化分析方法框架,该框架利用序列分析提取不同时期的活动模式,通过计算不同活动在时间点上的频率将活动模式可视化,并通过图形相似性对其进行分类,然后从活动和人口特征方面对活动模式进行比较。对 2001 年、2009 年和 2017 年全国家庭旅行调查(NHTS)的纽约大都市数据进行了实证检验。研究结果表明,纽约大都会不同社会人群在三个时期的活动模式存在明显的内相似性、间差异性和明显的变化。从信息分析的角度来看,这项工作有望加强对特定地区不同社会人群出行需求的了解,从而促进相关部门进行有针对性的政策调整。
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来源期刊
IET Intelligent Transport Systems
IET Intelligent Transport Systems 工程技术-运输科技
CiteScore
6.50
自引率
7.40%
发文量
159
审稿时长
3 months
期刊介绍: IET Intelligent Transport Systems is an interdisciplinary journal devoted to research into the practical applications of ITS and infrastructures. The scope of the journal includes the following: Sustainable traffic solutions Deployments with enabling technologies Pervasive monitoring Applications; demonstrations and evaluation Economic and behavioural analyses of ITS services and scenario Data Integration and analytics Information collection and processing; image processing applications in ITS ITS aspects of electric vehicles Autonomous vehicles; connected vehicle systems; In-vehicle ITS, safety and vulnerable road user aspects Mobility as a service systems Traffic management and control Public transport systems technologies Fleet and public transport logistics Emergency and incident management Demand management and electronic payment systems Traffic related air pollution management Policy and institutional issues Interoperability, standards and architectures Funding scenarios Enforcement Human machine interaction Education, training and outreach Current Special Issue Call for papers: Intelligent Transportation Systems in Smart Cities for Sustainable Environment - https://digital-library.theiet.org/files/IET_ITS_CFP_ITSSCSE.pdf Sustainably Intelligent Mobility (SIM) - https://digital-library.theiet.org/files/IET_ITS_CFP_SIM.pdf Traffic Theory and Modelling in the Era of Artificial Intelligence and Big Data (in collaboration with World Congress for Transport Research, WCTR 2019) - https://digital-library.theiet.org/files/IET_ITS_CFP_WCTR.pdf
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