CIAM: A data-driven approach for classifying long-term engagement of public transport riders at multiple temporal scales

Document Type

Journal Article

Publication Date

2022

Subject Area

place - urban, ridership - behaviour, ridership - modelling, technology - passenger information, technology - ticketing systems

Keywords

Public transport, Customer segmentation, Smart card data, Urban computing, Year-on-year churn, Monthly intensity, Annual engagement, Multi-year engagement, CIAM

Abstract

Many human activities, including daily travel, show a mix of stable, intermittent and changing patterns in demand by individuals over time. However, the lack of continuous, long-term, passenger-linked data for public transport (PT) journeys means that we do not know how passenger ridership evolves in real-world networks. This paper proposes the CIAM model for classifying long-term passenger engagement with PT. CIAM is a data-driven model combining year-on-year churn (C), monthly intensity (I), annual (A) and multi-year (M) engagement. Parameter search algorithms are used to ensure that the learned features are distinctive and robust. We evaluated CIAM using a 5-year dataset from a PT network with over 300 million journeys. CIAM identified distinct patterns of long-term ridership at multiple time scales. Although the total number of annual journeys was relatively stable over the five years, we found long-term differences between passenger subgroups. Churn of passengers was a major factor in ridership with only 55% of passengers retained from year to year. Patterns of annual engagement are often intermittent, so short-term snapshots of a few weeks are typically not good indicators for longer term engagement. Only 27% of high-frequency, full-fare riders still have the same level of engagement four years later, compared with 55% who continue high-frequency engagement after only one year.

Rights

Permission to publish the abstract has been given by Elsevier, copyright remains with them.

Comments

Transportation Research Part A Home Page:

http://www.sciencedirect.com/science/journal/09658564

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