Use of Smart Card Data to Define Public Transit Use in Seoul, South Korea

2008 ◽  
Vol 2063 (1) ◽  
pp. 3-9 ◽  
Author(s):  
Jin Young Park ◽  
Dong-Jun Kim ◽  
Yongtaek Lim
Keyword(s):  
South Korea ◽  
Smart Card ◽  
Public Transit ◽  
Smart Card Data ◽  
Transit Use

Analyzing Transit User Behavior with 51 Weeks of Smart Card Data

2019 ◽  
Vol 2673 (6) ◽  
pp. 33-45 ◽  
Author(s):  
Elodie Deschaintres ◽  
Catherine Morency ◽  
Martin Trépanier
Keyword(s):  
Smart Card ◽  
Public Transportation ◽  
Travel Behavior ◽  
Travel Demand ◽  
Public Transit ◽  
User Behavior ◽  
Individual Level ◽  
Smart Card Data ◽  
Demand Models ◽  
The Individual

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.

Public Transit Transfer Recognition Method Based on Vehicle GPS and Smart Card Data

CICTP 2017 ◽  
2018 ◽  
Author(s):  
Min Fu ◽  
Wei Wang ◽  
Hao Wang ◽  
Yun Xiang ◽  
Wanbo Yang
Keyword(s):  
Smart Card ◽  
Public Transit ◽  
Recognition Method ◽  
Smart Card Data

Smart card data use in public transit: A literature review

2011 ◽  
Vol 19 (4) ◽  
pp. 557-568 ◽  
Author(s):  
Marie-Pier Pelletier ◽  
Martin Trépanier ◽  
Catherine Morency
Keyword(s):  
Literature Review ◽  
Smart Card ◽  
Public Transit ◽  
Data Use ◽  
Smart Card Data

Measuring transit use variability with smart-card data

2007 ◽  
Vol 14 (3) ◽  
pp. 193-203 ◽  
Author(s):  
Catherine Morency ◽  
Martin Trépanier ◽  
Bruno Agard
Keyword(s):  
Smart Card ◽  
Smart Card Data ◽  
Transit Use

scholarly journals Investigating spatio-temporal mobility patterns and changes in metro usage under the impact of COVID-19 using Taipei Metro smart card data

2021 ◽  
Author(s):  
Christian Martin Mützel ◽  
Joachim Scheiner
Keyword(s):  
Smart Card ◽  
Public Transit ◽  
Mobility Patterns ◽  
Transit Planning ◽  
Transit Systems ◽  
Passenger Flow ◽  
Smart Card Data ◽  
Depth Analysis ◽  
Spatio Temporal ◽  
The Impact

AbstractModern public transit systems are often run with automated fare collection (AFC) systems in combination with smart cards. These systems passively collect massive amounts of detailed spatio-temporal trip data, thus opening up new possibilities for public transit planning and management as well as providing new insights for urban planners. We use smart card trip data from Taipei, Taiwan, to perform an in-depth analysis of spatio-temporal station-to-station metro trip patterns for a whole week divided into several time slices. Based on simple linear regression and line graphs, days of the week and times of the day with similar temporal passenger flow patterns are identified. We visualize magnitudes of passenger flow based on actual geography. By comparing flows for January to March 2019 and for January to March 2020, we look at changes in metro trips under the impact of the coronavirus pandemic (COVID-19) that caused a state of emergency around the globe in 2020. Our results show that metro usage under the impact of COVID-19 has not declined uniformly, but instead is both spatially and temporally highly heterogeneous.

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