Mining smart card data for transit riders’ travel patterns

By Xiaolei Ma, Yao-Jan Wu, Yinhai Wang, Feng Chen, Jianfeng Liu. 2013

601 citations recorded by the source corpus.

Subfields: Building and Construction, Transportation

View Paper: https://www.semanticscholar.org/paper/88328ceb2ee395569b06ff86b1314e6ff66e83fd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Yunpeng Wang
2. Xiaolei Ma
3. Yinhai Wang

In Transportation

1. Yunpeng Wang
2. Xiaolei Ma
3. Huimin Wen
4. Cynthia Chen
5. Menglin Wang
6. Yu Liu
7. Yusak O. Susilo
8. Yao-Jan Wu

Citations in the corpus

• Understanding commuting patterns using transit smart card data (2017, methodology): “In addition, targeting individual transit commuters using fare reduction for ridership attraction is necessary to improve public transportation systems usage rates (Ma et al., 2013).”
• The promises of big data and small data for travel behavior (aka human mobility) analysis (2016, background): “…sightings generated by phone operators for operation purposes (Calabrese et al., 2011), social media data generated voluntarily by users’ online activities (Chen and Schintler, forthcoming), and smartcard data collected at many transit systems worldwide (Pelletier et al., 2011; Ma et al., 2013).”
• Machine learning for Internet of Things data analysis: A survey (2017, methodology): “Furthermore, [47] compares KNN with a rough-set-based algorithm for classifying the travel pattern regularities.”