Mining smart card data for transit riders’ travel patterns

Ma, Xiaolei; Wu, Yao; Wang, Yinhai; Chen, Feng; Liu, Jianfeng

doi:10.1016/j.trc.2013.07.010

Cited by 515 publications

(247 citation statements)

References 30 publications

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“…One of the errors arises from data collecting, which may be caused by software bug, erroneous data, faulty hardware or the faulty operation [1,25,47,48]. In order to prevent this problem from happening, cleaning the data according different rules is essential.…”

Section: Possible Problems In Inferring Destinationmentioning

confidence: 99%

Smart Card Data Mining of Public Transport Destination: A Literature Review

Sun

Jing

et al. 2018

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Smart card data is increasingly used to investigate passenger behavior and the demand characteristics of public transport. The destination estimation of public transport is one of the major concerns for the implementation of smart card data. In recent years, numerous studies concerning destination estimation have been carried out-most automatic fare collection (AFC) systems only record boarding information but not passenger alighting information. This study provides a comprehensive review of the practice of using smart card data for destination estimation. The results show that the land use factor is not discussed in more than three quarters of papers and sensitivity analysis is not applied in two thirds of papers. In addition, the results are not validated in half the relevant studies. In the future, more research should be done to improve the current model, such as considering additional factors or making sensitivity analysis of parameters as well as validating the results with multi-source data and new methods.

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Section: Possible Problems In Inferring Destinationmentioning

confidence: 99%

Smart Card Data Mining of Public Transport Destination: A Literature Review

Sun

Jing

et al. 2018

Information

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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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“…Prominent examples are London (Oyster card) and Hong Kong (Octopus card), but many more examples are presented in literature (e.g. Seoul [16], Beijing [17], Santiago de Chile [18], Shenzen [19] and Brisbane [20]). An overview by [6] describes a range of smart card data applications, varying from strategic and tactical planning optimization to operational improvements.…”

Section: Automated Passenger Counts and Fare Validationmentioning

confidence: 99%

Improving Public Transport Decision Making, Planning and Operations by Using Big Data: Cases from Sweden and the Netherlands

Oort

Cats

2015

2015 IEEE 18th International Conference on Intelligent Transportation Systems

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ABSTRACT-New big data (sources) in the public transport industry enable to deal with major challenges such as elevating efficiency, increasing passenger ridership and satisfaction and facilitate the information flow between service providers and service users. This paper presents two actual cases from the Netherlands and Sweden in which automated data sources were utilized to support the planning and operational processes. The cases illustrate the benefits of using smartcard and vehicle positioning data. Due to the data (processing), valuable insights were gained helping to make the right choices and improve the public transport system.

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Mining smart card data for transit riders’ travel patterns

Cited by 515 publications

References 30 publications

Smart Card Data Mining of Public Transport Destination: A Literature Review

Smart Card Data Mining of Public Transport Destination: A Literature Review

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

Improving Public Transport Decision Making, Planning and Operations by Using Big Data: Cases from Sweden and the Netherlands

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