Analyzing Transit User Behavior with 51 Weeks of Smart Card Data

Abstract

A better understanding of mobility behaviors is relevant to many applications in public transportation, from more accurate travel demand models to improved supply adjustment, customized services and integrated pricing. In line with this context, this study mined 51 weeks of smart card (SC) data from Montréal, Canada to analyze interpersonal and intrapersonal variability in the weekly use of public transit. Passengers who used only one type of product (AP − annual pass, MP − monthly pass, or TB − ticket book) over 12 months were selected, amounting to some 200,000 cards. Data was first preprocessed and summarized into card-week vectors to generate a typology of weeks. The most popular weekly patterns were identified for each type of product and further studied at the individual level. Sequences of week clusters were constructed to represent the weekly travel behavior of each user over 51 weeks. They were then segmented by type of product according to an original distance, therefore highlighting the heterogeneity between passengers. Two indicators were also proposed to quantify intrapersonal regularity as the repetition of weekly clusters throughout the weeks. The results revealed MP owners have a more regular and diversified use of public transit. AP users are mainly commuters whereas TB users tend to be more occasional transit users. However, some atypical groups were found for each type of product, for instance users with 4-day work weeks and loyal TB users.

Systematic Literature Review on Smart Mobility: A Framework for Future “Quantitative” Developments

“…As regard the first meaning—the sharing of data and information—some studies describe the creation of collaborative mobility systems that allow vehicles and infrastructures to interconnect and share information to coordinate their actions (Autili et al 2019). Especially in large cities, the use of smart cards makes it possible to analyze interpersonal and intrapersonal variability in the weekly use of public transit (Deschaintres, Morency, and Trépanier 2019), therefore to infer the mobility models of urban collective transport services (Zhao et al 2019) and plan more efficient services that are close to citizens’ needs. Supporting mobility through automation processes can make the transport system so efficient as to manage traffic flows and adapt in real time by eliminating or minimizing total movements.…”

Methods for Travel Pattern Analysis Using Large-Scale Passive Data

“…Several studies have used k-means clustering or hierarchical clustering methods to Chapter 4. Methods for Processing Large-Scale Passive Data group passengers by their temporal patterns (Viallard et al, 2019;Deschaintres et al, 2019;Egu and Bonnel, 2020). Some authors also include spatial patterns such as the regular use of the same route to group passengers (Manley et al, 2018;Morency et al, 2007).…”

“…It is also possible to use clustering (see Chapter 4.2) to automatically identify groups for aggregation. An example is identifying different groups of travellers by their travel behaviour (Deschaintres et al, 2019). An essential tool to understand and draw conclusions from the data is visualisation.…”

Spatiotemporal Dynamics of Public Transport Demand: A Case Study of Riga

“…Tao et al (2014) compared origins and destinations distribution over the time to discover patterns for different social groups of passengers. Similar to studies on boarding information, origin and destination pairs were recently used for passenger and weeks clustering (Deschaintres et al 2019;He et al 2020).…”