|Clustering the Multi-week Activity Sequences of Public Transport Users
|Year of Publication
|Gabriel Goulet Langlois, Haris Koutsopoulos, Jinhua Zhao
|95th Transportation Research Board Annual Meeting
|Transportation Research Board
|Activity sequence, Automatic Fare Collection data, Public transportation, smart card, Transit, Travel behavior, Travel patterns, User clustering
The public transport networks of dense cities such as London serve passengers with widely dierent travel patterns. In line with the diverse lives of urban dwellers, activities and journeys are combined within days and across days in diverse sequences. From personalized customer information, to improved travel demand models, understanding this type of heterogeneity among transit users is relevant to an number of applications core to public transport agencies' function. In this study, passenger heterogeneity is investigated based on a longitudinal representation of each user's multi-week activity sequence derived from smart card data. We propose a methodology leveraging this representation to identify clusters of users with similar activity sequence structure. The methodology is applied to a large sample from London's public transport network, in which each passenger is represented by a continuous 4-week activity sequence. The application reveals 11 clusters, each characterized by a distinct sequence structure. Socio-demographic information available for a small sample of users is combined to smart card transactions to analyze associations between the identied patterns and demographic attributes including passenger age, occupation, household composition and income, and vehicle ownership. The analysis reveals that signicant connections exist between the demographic attributes of users and activity patterns identied exclusively from fare transactions.