Research on the Stability of Public Transit Passenger Travel Behavior Based on Smart Card Data

CICTP 2014 ◽  
2014 ◽  
Author(s):  
Cheng-liang Cui ◽  
Ya-li Zhao ◽  
Zheng-yu Duan
Keyword(s):  
Smart Card ◽  
Travel Behavior ◽  
Public Transit ◽  
Passenger Travel ◽  
Smart Card Data ◽  
Behavior Based ◽  
The Stability

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.

scholarly journals Integrating Geovisual Analytics with Machine Learning for Human Mobility Pattern Discovery

2019 ◽  
Vol 8 (10) ◽  
pp. 434 ◽  
Author(s):  
Tong Zhang ◽  
Jianlong Wang ◽  
Chenrong Cui ◽  
Yicong Li ◽  
Wei He ◽  
...  
Keyword(s):  
Machine Learning ◽  
Smart Card ◽  
Travel Behavior ◽  
Public Transit ◽  
Travel Patterns ◽  
Mobility Patterns ◽  
Learning Methods ◽  
Transit Network ◽  
Machine Learning Methods ◽  
Smart Card Data

Understanding human movement patterns is of fundamental importance in transportation planning and management. We propose to examine complex public transit travel patterns over a large-scale transit network, which is challenging since it involves thousands of transit passengers and massive data from heterogeneous sources. Additionally, efficient representation and visualization of discovered travel patterns is difficult given a large number of transit trips. To address these challenges, this study leverages advanced machine learning methods to identify time-varying mobility patterns based on smart card data and other urban data. The proposed approach delivers a comprehensive solution to pre-process, analyze, and visualize complex public transit travel patterns. This approach first fuses smart card data with other urban data to reconstruct original transit trips. We use two machine learning methods, including a clustering algorithm to extract transit corridors to represent primary mobility connections between different regions and a graph-embedding algorithm to discover hierarchical mobility community structures. We also devise compact and effective multi-scale visualization forms to represent the discovered travel behavior dynamics. An interactive web-based mapping prototype is developed to integrate advanced machine learning methods with specific visualizations to characterize transit travel behavior patterns and to enable visual exploration of transit mobility patterns at different scales and resolutions over space and time. The proposed approach is evaluated using multi-source big transit data (e.g., smart card data, transit network data, and bus trajectory data) collected in Shenzhen City, China. Evaluation of our prototype demonstrates that the proposed visual analytics approach offers a scalable and effective solution for discovering meaningful travel patterns across large metropolitan areas.

Uncovering quasi-periodicity of transit behavior based on smart card data

2019 ◽  
Vol 79 ◽  
pp. 102466
Author(s):  
Jiaming Zhong ◽  
Zhaocheng He ◽  
Chenyu Tian
Keyword(s):  
Smart Card ◽  
Smart Card Data ◽  
Behavior Based

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
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