Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/34774
Title: Identifying business activity-travel patterns based on GPS data
Authors: LIU, Feng 
Gao, ZY
JANSSENS, Davy 
WETS, Geert 
Jia, B
Yang, Y
Issue Date: 2021
Publisher: PERGAMON-ELSEVIER SCIENCE LTD
Source: Transportation research. Part C, Emerging technologies, 128 , Art. N° 103136
Abstract: As employers, suppliers, and transport providers, organisations generate a large portion of traffic flows on transport networks. However, despite the significance of business travel to overall mobility, the underlying activity compositions of the movement and decision-making processes within organisations are not well understood. In this study, a new method is developed based on GPS data to identify typical business activity-travel patterns and characteUsing GPS data collected from the real operation of 6,500 commercial vehicles over a period of three months, the proposed method was tested. In total, five profiles were constructed, accommodating activity-travel patterns associated with vans, cars, trucks-35 t (light trucks), trucks-3ax (medium trucks), and buses. Similarities and differences in these profiles across vehicle types were revealed, and specific locations corresponding to the activities of the patterns were further examined. Moreover, using these profiles as a reference, the travel practice of a specific vehicle was evaluated. The experimental results demonstrate the potential and effectiveness of the approach in depicting business travel patterns, providing a deep understanding of business travel behaviour, and assisting the design and evaluation of policies for more sustainable business transport.
Keywords: GPS data;Activity-travel patterns;Business travel behaviour;Sequential Pattern Mining;Sequence Alignment Methods
Document URI: http://hdl.handle.net/1942/34774
ISSN: 0968-090X
e-ISSN: 1879-2359
DOI: 10.1016/j.trc.2021.103136
ISI #: 000662797300008
Rights: © 2021 Elsevier Ltd. All rights reserved
Category: A1
Type: Journal Contribution
Validations: ecoom 2022
Appears in Collections:Research publications

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