Analyzing the impact of fare-free public transport policies on crowding patterns at stations using crowdsensing data

Fully or partially fare-free public transport (FFPT) is a measure to make public transport (PT) more attractive and affordable. Cities worldwide are experimenting with a variety of fare discount policies, which will lead to spatiotemporal changes in mobility patterns, including crowding in PT stations. However, the response of PT stations to these policies can vary due to the heterogeneity in their surrounding built environment and other factors. Non-conventional data sources could be used to better model and understand these spatiotemporal dynamics. In this study, we propose a three-step methodological framework to understand the impact of FFPT or extreme fare discount interventions on PT demand patterns, specifically focusing on crowding patterns in PT stations, using crowdsensing data. First, we design a busyness-based similarity measure that leverages the histogram method to capture changes in crowding patterns. Then, we employ a Gaussian Mixture Model (GMM) to cluster PT stations based on their crowding pattern deviations at different stages of policy implementation. This clustering step enables the identification of distinct station types based on their response to the policy. Finally, we train a LightGBM model to learn the relationship between crowding pattern changes and the spatial–temporal characteristics of PT stations, using the busyness-based station types identified by GMM as labels. We apply our methodology to a public transport experiment in Germany during the summer of 2022 when the country introduced a monthly “9-EUR” ticket valid on local and regional PT nationwide. The clustering results show three station types: unaffected, mildly stimulated, and intensely stimulated stations. Furthermore, the classifier indicates that the station’s location, activity options near the station, and population within the adjacent area of the station, and the crowding patterns under normal operations (before policy implementation) play a significant role in the heterogeneity of the 9-EUR ticket’s impact. The insights gained from our study can help planners better understand and manage the crowding at PT stations during fare interventions.