scholarly journals Investigating the Correlation between Activity Similarity and Trip Similarity of Public Transit Passengers Using Smart Card Data

2020 ◽  
Vol 48 ◽  
pp. 2621-2637
Author(s):  
Hamed Faroqi ◽  
Mahmoud Mesbah ◽  
Jiwon Kim
Author(s):  
Elodie Deschaintres ◽  
Catherine Morency ◽  
Martin Trépanier

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.


CICTP 2017 ◽  
2018 ◽  
Author(s):  
Min Fu ◽  
Wei Wang ◽  
Hao Wang ◽  
Yun Xiang ◽  
Wanbo Yang

2011 ◽  
Vol 19 (4) ◽  
pp. 557-568 ◽  
Author(s):  
Marie-Pier Pelletier ◽  
Martin Trépanier ◽  
Catherine Morency

2021 ◽  
Author(s):  
Christian Martin Mützel ◽  
Joachim Scheiner

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.


2020 ◽  
Vol 120 ◽  
pp. 103288
Author(s):  
Sumit Agarwal ◽  
Mi Diao ◽  
Jussi Keppo ◽  
Tien Foo Sing

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