详情

原文传递 Exploring Activity Patterns and Trip Purposes of Public Transport Passengers from Smart Card Data

题名：	Exploring Activity Patterns and Trip Purposes of Public Transport Passengers from Smart Card Data
正文语种：	eng
作者：	Yanyan Chen;Zifan Wang;Haodong Sun;Jiachen Wang
作者单位：	Beijing Key Laboratory of Traffic Engineering Beijing Univ. of Technology Beijing 100124 China;Beijing Key Laboratory of Traffic Engineering Beijing Univ. of Technology Beijing 100124 China;Beijing Key Laboratory of Traffic Engineering Beijing Univ. of Technology Beijing 100124 China;Faculty of Information Technology Beijing Univ. of Technology Beijing 100124 China
关键词：	Activity pattern; Trip purpose; Latent Dirichlet allocation (LDA) topic model; Smart card data; Point of interest data
摘要：	With the development of technology, there is an increasing number of automatically collected data sources applied in human mobility research. Although these data sets can record travel spatiotemporal information, the semantical information of travel cannot be reflected, e.g., activity pattern or trip purpose. In this paper, we proposed a methodological framework to explore the activity patterns and trip purposes of public transit riders using smart card data in an unsupervised way. First, the heuristic rules are proposed to identify home/working activity based on the multidays travel regularity of passengers. Second, we use a modified latent Dirichlet allocation (LDA) model to explore the activity patterns for the remaining activities based on four activity attributes (including arrival time, duration, day of the week, and destination station functional attribute). In this model, the trip attributes of each passenger are considered as a word in the document with specific topics that correspond to different activity characteristics, based on which the trip purpose of each topic is inferred to interpret travel behavior. The proposed methodology is demonstrated using transit smart card data from Beijing. The performance of our model is compared with two baselines based on perplexity and the result shows that our model achieved the best. Besides, the proportions of inferred trip purposes are compared to the values from travel survey in 2020 Beijing Transport Development Annual Report. The reliability of the results is further confirmed. This work can be extended to other automated travel datasets without ground-truth labels and used to understand and predict travel demand.
出版年：	2023
期刊名称：	Journal of Transportation Engineering
卷：	149
期：	9
页码：	04023076.1-04023076.14