Resident activity pattern recognition and comparison of six Sino-American metropolises

• Published in: IET intelligent transport systems 2019-03, Vol.13 (3), p.443-452
• Main Authors: Yang, Chao, Ye, Wen, Zhu, Rongrong, Zhang, Tianran
• Format: Article
• Language: English
• Subjects: activity‐topic probability distribution; affinity propagation clustering; decision making; latent Dirichlet allocation topic model; National Household Travel Survey data of USA; number‐of‐per capita activities; pattern characteristics; pattern clustering; RAP recognition; representative activity patterns; sex ratio; Shanghai Household Travel Survey data; Sino‐American metropolises; Special Issue: Big Data Analytics and Artificial Intelligence (AI) Applications for Smart Transportation - Selected Papers from World Transportation Congress (WTC) 2018; statistical distributions; traffic engineering computing; traffic‐activity characteristics; travel decision making; travel industry; weekdays late return work pattern; weekdays normal work pattern; weekends work pattern
• ISSN: 1751-956X; 1751-9578; 1751-9578
• DOI: 10.1049/iet-its.2018.5246

Travel decision making is driven by different activities. To better understand the traffic-activity characteristics of different regions, representative activity patterns (RAPs) of six Sino-American metropolises are recognised, and then these metropolises are clustered for each kind of RAP. Same year's National Household Travel Survey data of USA and Shanghai Household Travel Survey (SHTS) data are used in this study. Each resident's activity-topic probability distribution could be obtained by latent Dirichlet allocation topic model, then residents’ RAPs of a region are recognised by affinity propagation clustering, for each region's weekdays and weekends, respectively. Based on RAPs recognition, the six metropolises are clustered within conspecific RAP according to the dissimilarity of pattern characteristics (the number of per capita activities, sex ratio etc.). At last, six metropolises are divided into three, two, five clusters in accordance with three kinds of work patterns, which are weekdays’ normal work pattern, weekdays’ late return (home) work pattern, and weekends’ work pattern. Regions in the same cluster have homologous demographic compositions and show similar activity characteristics.