Investigating spatial-temporal characteristics of joint activity/travel behaviour with smart card data

IF 5.1 2区 工程技术 Q1 TRANSPORTATION Travel Behaviour and Society Pub Date : 2024-09-06 DOI:10.1016/j.tbs.2024.100899
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Abstract

The process of urbanization and the rapid development of transit networks transform urban land use properties and individuals’ travel demand, especially in densely populated areas. Traffic surveys reveal that joint activity/travel behaviour constitutes an increasingly significant portion of overall travel behaviours. This paper aims to investigate the spatial–temporal characteristics of individuals’ joint activity/travel patterns (JATPs) within the subway network and explores the factors that influence individuals’ choices among different activity/travel patterns. First, we establish a social relationship classifier to infer acquaintances among individuals. Subsequently, we define four distinct types of JATPs and introduce an algorithm for recognizing these patterns. To implement the proposed framework, we conduct a real-world case study based on subway smart card data from Nanjing, China. Our findings reveal that approximately 19% of daily trips were identified as JATPs, while different JATPs exhibit diverse spatial and temporal characteristics. These differences can be attributed to various influencing factors, including travel/activity time, passenger flow, departure time, and passenger flow/income around tap-in/out stations. In addition, we employ the machine learning classification method XGBoost to predict the discrete choice process of JATP. The classification results reveal the spatial–temporal characteristics of JATP and examine the potential influences of various factors on individuals’ joint activity and travel behaviours. Ultimately, our research sheds light on the evolving landscape of urban travel behaviour in the context of joint activities and travel, with practical implications for transportation planning and policy.

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利用智能卡数据调查联合活动/旅行行为的时空特征
城市化进程和公交网络的快速发展改变了城市土地使用性质和个人的出行需求,尤其是在人口稠密地区。交通调查显示,联合活动/出行行为在整体出行行为中所占比例越来越大。本文旨在研究地铁网络中个人联合活动/旅行模式(JATPs)的时空特征,并探讨影响个人选择不同活动/旅行模式的因素。首先,我们建立了一个社会关系分类器来推断个体之间的熟人关系。随后,我们定义了四种不同类型的 JATP,并介绍了一种识别这些模式的算法。为了实现所提出的框架,我们基于中国南京的地铁智能卡数据进行了实际案例研究。我们的研究结果表明,约有 19% 的日常出行被识别为 JATP,而不同的 JATP 表现出不同的空间和时间特征。这些差异可归因于各种影响因素,包括乘车/活动时间、客流量、出发时间以及进出站周边的客流量/收入。此外,我们还采用了机器学习分类方法 XGBoost 来预测 JATP 的离散选择过程。分类结果揭示了 JATP 的时空特征,并研究了各种因素对个人联合活动和出行行为的潜在影响。最终,我们的研究揭示了在联合活动和出行背景下城市出行行为的演变过程,对交通规划和政策具有实际意义。
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来源期刊
CiteScore
9.80
自引率
7.70%
发文量
109
期刊介绍: Travel Behaviour and Society is an interdisciplinary journal publishing high-quality original papers which report leading edge research in theories, methodologies and applications concerning transportation issues and challenges which involve the social and spatial dimensions. In particular, it provides a discussion forum for major research in travel behaviour, transportation infrastructure, transportation and environmental issues, mobility and social sustainability, transportation geographic information systems (TGIS), transportation and quality of life, transportation data collection and analysis, etc.
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