Study group travel behaviour patterns from large-scale smart card data

Study group travel behaviour patterns from large-scale smart card data

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 eff...

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Bibliographic Details
Main Authors: TIAN, Xiancai, ZHENG, Baihua
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2019
Subjects:
smart card data
spatial and temporal systems
group travel behaviour
bloom filter
Databases and Information Systems
Urban Studies
Online Access:https://ink.library.smu.edu.sg/sis_research/4614
https://ink.library.smu.edu.sg/context/sis_research/article/5617/viewcontent/Study_Group_Travel_Behaviour_From_Smart_Card_Data__Camera_Ready_.pdf
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Institution: Singapore Management University
Language: English
Description
Summary: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.

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