Measuring transit use variability with smart-card data

Morency, Catherine; Trépanier, Martin; Agard, Bruno

doi:10.1016/j.tranpol.2007.01.001

Cited by 228 publications

(96 citation statements)

References 22 publications

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“…They proposed a method to extract ridership and operation information for better transit management by using the data in Istanbul, Turkey. Morency et al [4] focused on the measurement of transit use variability. They used Canadian transit network data from 277 consecutive days and applied data mining techniques for measuring spatial and temporal variability of transit use for different types of cards.…”

Section: Introductionmentioning

confidence: 99%

Behavioural analysis of railway passengers using smart card data

Asakura

Iryo

Nakajima

et al. 2008

WIT Transactions on the Built Environment

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Smart card systems have been spreading globally not only for purchasing general goods and services, but also for fare payment of public transport. When an individual traveller is holding a public transport smart card, the precise times of his/her passing through ticket gates are recorded in the smart card system. The longitudinal smart card data of a large number of passengers have been stored by a public transport company. The aims of this paper are to analyze the behaviour of railway passengers using the smart card data, and to evaluate the effects of train operation policies. The analysis is focused on the before and after comparison of travel choice behaviour of passengers when the railway company changed the train timetable.The individual passenger's passing times at the entrance and exit ticket gates of origin and destination stations were aggregated for a small discrete time interval in a day. Through analyzing the departure and arrival time distribution and the travel time distribution, it was found that the passengers smoothly adjusted their travel behaviour for the improved train timetable. When the passing times of ticket gates of both origin and destination stations are combined with the train timetable, it becomes possible to identify the train that each traveller was likely on board. Through the before and after comparison, it is shown how the railway passengers have changed their train choosing behaviour for the improved time schedules.

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Section: Introductionmentioning

confidence: 99%

Behavioural analysis of railway passengers using smart card data

Asakura

Iryo

Nakajima

et al. 2008

WIT Transactions on the Built Environment

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“…Spatial and temporal regularity of travelers was measured by researchers in the past by grouping them by chosen boarding/alighting stops and routes on different weekdays, and by grouping them by time of travel [11][12][13]. Morency et al were further interested in the class wise regularity patterns of travelers [11,12].…”

Section: Related Workmentioning

confidence: 99%

“…Travelers for age occupation wise travel behavior [11] k-means 277 consecutive days Travelers for regularity in boarding [12] k-means 277 consecutive days Mining travel patterns [13] DBSCAN 5 consecutive weekdays Origin-destination pairs for discovering zones based on movement patterns [15] Clustering 5 consecutive weekdays…”

Section: Geographical Clusteringmentioning

confidence: 99%

Flow Orientation Analysis for Major Activity Regions Based on Smart Card Transit Data

Singh

Jung

2017

IJGI

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Analyzing public movement in transportation networks in a city is significant in understanding the life of citizen and making improved city plans for the future. This study focuses on investigating the flow orientation of major activity regions based on smart card transit data. The flow orientation based on the real movements such as transit data can provide the easiest way of understanding public movement in the complicated transportation networks. First, high inflow regions (HIRs) are identified from transit data for morning and evening peak hours. The morning and evening HIRs are used to represent major activity regions for major daytime activities and residential areas, respectively. Second, the directional orientation of flow is then derived through the directional inflow vectors of the HIRs to show the bias in directional orientation and compare flow orientation among major activity regions. Finally, clustering analysis for HIRs is applied to capture the main patterns of flow orientations in the city and visualize the patterns on the map. The proposed methodology was illustrated with smart card transit data of bus and subway transportation networks in Seoul, Korea. Some remarkable patterns in the distribution of movements and orientations were found inside the city. The proposed methodology is useful since it unfolds the complexity and makes it easy to understand the main movement patterns in terms of flow orientation.

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“…Since then, various researches and case studies were done on the data collected from public transport smart card systems around the world. Morency et al [3] explored the data mining techniques used to analyze the spatial and temporal variability of Canadian pubic transit network passengers using different card types. Asakura et al [4], constructed a origin-destination (O-D) matrix using the smart card data collected from Japan's public train network and applied statistical analysis to study the change in passengers' travel patterns when the train operator changed its train timetable.…”

Section: Literature Reviewmentioning

confidence: 99%