Smart card data gathered by Automated Fare Collection (AFC) systems are a valuable resource for studying urban mobility. In this paper, we propose two approaches to clustering smart card data that can be used to extract mobility patterns in a public transportation system. Two complementary standpoints are considered: a station-oriented, operational point of view and a passenger-focused one. The first approach clusters stations based on when their activity occurs, i.e. how trips made at the stations are distributed over time. The second approach makes it possible to identify groups of passengers that have similar boarding times aggregated into weekly profiles. By applying our approaches to a real dataset issued from the metropolitan area of Rennes (France) we illustrate how they can help reveal valuable insights about urban mobility like the presence of different station key-roles such as residential stations used mostly in the mornings, work stations used only in the evening and almost exclusively during weekdays, etc. as well as different passenger behaviors ranging from the sporadic and diffuse usage to typical commute practices. By cross-comparing passenger clusters with fare types, we also highlight how certain usages are more specific to particular types of passengers
Abstract. Even though clustering trajectory data attracted considerable attention in the last few years, most of prior work assumed that moving objects can move freely in an euclidean space and did not consider the eventual presence of an underlying road network and its influence on evaluating the similarity between trajectories. In this paper, we present two approaches to clustering network-constrained trajectory data. The first approach discovers clusters of trajectories that traveled along the same parts of the road network. The second approach is segment-oriented and aims to group together road segments based on trajectories that they have in common. Both approaches use a graph model to depict the interactions between observations w.r.t. their similarity and cluster this similarity graph using a community detection algorithm. We also present experimental results obtained on synthetic data to showcase our propositions.
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