A visual segmentation method for temporal smart card data

Ghaemi, Mohammad Sajjad; Agard, Bruno; Trépanier, Martin; Nia, Vahid Partovi

doi:10.1080/23249935.2016.1273273

Cited by 37 publications

(14 citation statements)

References 26 publications

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“…The heat-map method visually unveils the spatial-temporal travel demand patterns at a regional level. Ghaemi et al [22] presented Mathematical Problems in Engineering 3…”

Section: Literature Reviewmentioning

confidence: 99%

Comparing Sequential with Combined Spatiotemporal Clustering of Passenger Trips in the Public Transit Network Using Smart Card Data

Faroqi

Mesbah

Kim

2019

Mathematical Problems in Engineering

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Smart card datasets in the public transit network provide opportunities to analyse the behaviour of passengers as individuals or as groups. Studying passenger behaviour in both spatial and temporal space is important because it helps to find the pattern of mobility in the network. Also, clustering passengers based on their trips regarding both spatial and temporal similarity measures can improve group-based transit services such as Demand-Responsive Transit (DRT). Clustering passengers based on their trips can be carried out by different methods, which are investigated in this paper. This paper sheds light on differences between sequential and combined spatial and temporal clustering alternatives in the public transit network. Firstly, the spatial and temporal similarity measures between passengers are defined. Secondly, the passengers are clustered using a hierarchical agglomerative algorithm by three different methods including sequential two-step spatial-temporal (S-T), sequential two-step temporal-spatial (T-S), and combined one-step spatiotemporal (ST) clustering. Thirdly, the characteristics of the resultant clusters are described and compared using maps, numerical and statistical values, cross correlation techniques, and temporal density plots. Furthermore, some passengers are selected to show how differently the three methods put the passengers in groups. Four days of smart card data comprising 80,000 passengers in Brisbane, Australia, are selected to compare these methods. The analyses show that while the sequential methods (S-T and T-S) discover more diverse spatial and temporal patterns in the network, the ST method entails more robust groups (higher spatial and temporal similarity values inside the groups).

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“…The heat-map method visually unveils the spatial-temporal travel demand patterns at a regional level. Ghaemi et al [22] presented Mathematical Problems in Engineering 3…”

Section: Literature Reviewmentioning

confidence: 99%

Comparing Sequential with Combined Spatiotemporal Clustering of Passenger Trips in the Public Transit Network Using Smart Card Data

Faroqi

Mesbah

Kim

2019

Mathematical Problems in Engineering

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“…A variety of clustering methods have been explored so far. Agglomerative hierarchical clustering has been employed to determine periods of homogeneous flow [56] and distinguish users with similar temporal behavior [57][58][59]. Similarly, a large body of literature has applied k-means clustering to identify regular spatial and temporal patterns [60][61][62] and understand social interactions between transit users [63].…”

Section: Activity Modelingmentioning

confidence: 99%

“…These rely on arbitrary thresholds and parameter values under some type of contextual information or user preferences. On the contrary, although harder to design and implement, model-based clustering methods can adapt to more complex data patterns and can be used in conjunction with travel demand simulation models [57,69]. Along the same lines, machine learning algorithms can be applied prior to segmentation, to transition from user-specified parameters to datadriven inference [104].…”

Section: Tactical Level Planningmentioning

confidence: 99%

Combining ITS and optimization in public transportation planning: state of the art and future research paths

Iliopoulou

Κεπαπτσόγλου

2019

Eur. Transp. Res. Rev.

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Intelligent Transportation Systems (ITS) applications in public transportation have allowed for automated data collection, which is particularly useful for planning and operations. While technological advancement of ITS has so far been extensive, their usage for developing relevant planning and operational tools is rather limited. Research on planning and operations of public transportation systems has not widely investigated the potential of combining optimization models with data originating from ITS. Such applications, which could benefit from such an approach include route planning, scheduling and resource allocation in real time. In this context, this paper investigates and critically discusses potential models and methodologies in public transport planning and operations, which can benefit from ITS data, highlights their potential and identifies possible research paths on that area. The overview of literature collectively points to a series of common challenges faced by transportation professionals and underlines the need for better decision support tools for ITS data.

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“…Nowadays, changes in society make this especially relevant. Indeed, while services are generally suitable for regular workers who travel 5 days a week during peak hours, other behaviors appear because of the development of teleworking and 4-day work weeks ( 18 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Analyzing Transit User Behavior with 51 Weeks of Smart Card Data

Deschaintres

Morency

Trépanier

2019

Transportation Research Record

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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.

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A visual segmentation method for temporal smart card data

Cited by 37 publications

References 26 publications

Comparing Sequential with Combined Spatiotemporal Clustering of Passenger Trips in the Public Transit Network Using Smart Card Data

Comparing Sequential with Combined Spatiotemporal Clustering of Passenger Trips in the Public Transit Network Using Smart Card Data

Combining ITS and optimization in public transportation planning: state of the art and future research paths

Analyzing Transit User Behavior with 51 Weeks of Smart Card Data

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