Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
12-2019
Abstract
In this paper, we aim at studying the group travel behaviour (GTB) patterns from large-scale auto fare collection (AFC) data. GTB is defined as two or more commuters intentionally and regularly traveling together from an origin to a destination. We propose a method to identify GTB accurately and efficiently and apply our method to the Singapore AFC dataset to reveal the GTB patterns of Singapore commuters. The case study proves that our method is able to identify GTB patterns more accurately and efficiently than the state-of-the-art.
Keywords
smart card data, spatial and temporal systems, group travel behaviour, bloom filter
Discipline
Databases and Information Systems | Urban Studies
Research Areas
Data Science and Engineering
Publication
2019 IEEE International Conference on Big Data: December 9-12, Los Angeles: Proceedings
First Page
1232
Last Page
1237
ISBN
9781728108582
Identifier
10.1109/BigData47090.2019.9005575
Publisher
IEEE Computer Society
City or Country
Los Alamitos, CA
Citation
TIAN, Xiancai and ZHENG, Baihua.
Study group travel behaviour patterns from large-scale smart card data. (2019). 2019 IEEE International Conference on Big Data: December 9-12, Los Angeles: Proceedings. 1232-1237.
Available at: https://ink.library.smu.edu.sg/sis_research/4614
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/BigData47090.2019.9005575