Station-based bike sharing (SBBS) not only provides commuters with direct “door-to-door” trips, but also plays a vital role in addressing the “first/last mile” challenges for public transportation system. However, there is a lack of research into portraying year-to-year changes in SBBS commuter behaviors. With five-year (from 2016 to 2020) SBBS smart card data collected in Nanjing, China, a longitudinal analysis is performed in this study to trace yearly dynamics of commuter behaviors at an individual level. We identify two sorts of SBBS commuters (i.e., SBBS-alone and SBBS-metro commuters) based on users’ spatial-temporal travel regularities. The paper finds that (i) the number of SBBS users presented a considerable fluctuation trend over a five-year span, while the proportion of SBBS commuters stabilized at an equilibrium level; (ii) the COVID-19 outbreak accelerated the decline in the proportion of female and young SBBS commuters; (iii) most SBBS commuters were recorded for only one year out of five, while the share of commuters who used SBBS for four years or more is tiny, <5%; (iv) the trip duration of SBBS-alone commuters was significantly longer than that of SBBS-metro commuters, and both showed some increase during the COVID-19 pandemic; (v) the number of non-loop trip chains was dramatically higher than that of loop trip chains, which is more prominent among SBBS-metro commuters. Our findings could provide valuable insights into the behavioral dynamics of SBBS commuters and offer recommendations on how policy makers and transportation planners could respond to these precipitate changes. 

Exploring year-to-year changes in station-based bike sharing commuter behaviors with smart card data

In recent years, a growing number of large, densely populated cities have emerged, which need urban traffic planning and therefore knowledge of mobility patterns. Knowledge of space-time distribution of passengers in cities is necessary for effective urban traffic planning and restructuring, especially in large cities. In this paper, the inbound ridership in the Barcelona metro is modelled into a three-way tensor so that each element contains the number of passenger in the ith station at the jth time on the kth day. Tucker3 decomposition is used to discover spatial clusters, temporal patterns, and the relationships between them. The results indicate that travel patterns differ between weekdays and weekends; in addition, rush and off-peak hours of each day have been identified, and a classification of stations has been obtained.

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An Analysis of Travel Patterns in Barcelona Metro Using Tucker3 Decomposition

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. • CIAM learns long-term patterns of PT engagement at multiple temporal scales. • Churn is a major factor of ridership with year on year passenger retention of only 55%. • Annual engagement is a mixture of contrasting short-term intensities. • Only 27% of high-frequency, full-fare passengers maintain that engagement for 5 years. • Data-driven CIAM identifies underlying context transitions from movement data alone. 

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

Human mobility is subject to collective dynamics that are the outcome of numerous individual choices. Smart card data which originated as a means of facilitating automated fare collection has emerged as an invaluable source for analysing human mobility patterns. A variety of clustering and segmentation techniques has been adopted and adapted for applications ranging from passenger demand market segmentation to the analysis of urban activity locations. In this paper we provide a systematic review of the state-of-the-art on clustering public transport users based on their temporal or spatial-temporal characteristics as well as studies that use the latter to characterise individual stations, lines or urban areas. Furthermore, a critical review of the literature reveals an important distinction between studies focusing on the intra-personal variability of travel patterns versus those concerned with the interpersonal variability of travel patterns. We synthesize the key analysis approaches and based on which identify and outline the following directions for further research: (i) predictions of passenger travel patterns; (ii) decision support for service planning and policy evaluation; (iii) enhanced geographical characterisation of users’ travel patterns; (iv) from demand analytics towards behavioural analytics. following performing the patterns’, and ‘Spatiotemporal’.

Identifying Human Mobility Patterns using Smart Card Data

The analysis of longitudinal travel data enables investigating how mobility patterns vary across the population and identify the spatial properties thereof. The objective of this study is to identify the extent to which users explore different parts of the network as well as identify distinctive user groups in terms of the spatial extent of their mobility patterns. To this end, we propose two means for representing spatial mobility profiles and clustering travellers accordingly. We represent users patterns in terms of zonal visiting frequency profiles and grid-cells spatial extent heatmaps. We apply the proposed analysis to a large-scale multi-modal mobility dataset from the public transport system in Stockholm, Sweden. We unravel three clusters - Locals, Commuters and Explorers - that best describe the zonal visiting frequency and show that their composition varies considerably across users’ place of residence. We also identify 15 clusters of visiting spatial extent based on the intensity and direction in which they are oriented. A cross-analysis of the results of the two clustering methods reveals that user segmentation based on exploration patterns and spatial extent are largely independent, indicating that the two different clustering approaches provide fundamentally different insights into the underlying spatial properties of individuals’ mobility patterns. The approach proposed and demonstrated in this study could be applied for any longitudinal individual travel demand data. 

Unravelling the spatial properties of individual mobility patterns using longitudinal travel data

To better understand long-term patterns of human mobility, this study examines changes in travel behavior at the individual level based on yearly activity profiles using 3 years of longitudinal smart card data collected in Shizuoka, Japan. We first characterize spatiotemporal patterns of railway usage by k-means clustering, and then investigate variation in cluster membership with time. For among passengers who remained active, regular commuters had similar travel patterns over the study period, whereas infrequent travelers significantly increased their use of the railway system. The evolution of cluster assignment is analyzed and discussed.

Takenori Iwamoto

Papers

Investigation of Changes in Passenger Behavior Using Longitudinal Smart Card Data