research-article

Rationality Analytics from Trajectories

Authors:

Shuhui WangAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 10, Issue 1

Article No.: 10, Pages 1 - 22

https://doi.org/10.1145/2735634

Published: 22 July 2015 Publication History

Abstract

The availability of trajectories tracking the geographical locations of people as a function of time offers an opportunity to study human behaviors. In this article, we study rationality from the perspective of user decision on visiting a point of interest (POI) which is represented as a trajectory. However, the analysis of rationality is challenged by a number of issues, for example, how to model a trajectory in terms of complex user decision processes? and how to detect hidden factors that have significant impact on the rational decision making? In this study, we propose Rationality Analysis Model (RAM) to analyze rationality from trajectories in terms of a set of impact factors. In order to automatically identify hidden factors, we propose a method, Collective Hidden Factor Retrieval (CHFR), which can also be generalized to parse multiple trajectories at the same time or parse individual trajectories of different time periods. Extensive experimental study is conducted on three large-scale real-life datasets (i.e., taxi trajectories, user shopping trajectories, and visiting trajectories in a theme park). The results show that the proposed methods are efficient, effective, and scalable. We also deploy a system in a large theme park to conduct a field study. Interesting findings and user feedback of the field study are provided to support other applications in user behavior mining and analysis, such as business intelligence and user management for marketing purposes.

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https://doi.org/10.1038/s41598-023-45995-3
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Index Terms

Rationality Analytics from Trajectories
1. Information systems
  1. Information systems applications

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 10, Issue 1

July 2015

321 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/2808688

Editor:
Philip S. Yu
University of Illinois at Chicago, USA

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Copyright © 2015 ACM.

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Association for Computing Machinery

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Publication History

Published: 22 July 2015

Accepted: 01 February 2015

Revised: 01 January 2015

Received: 01 October 2014

Published in TKDD Volume 10, Issue 1

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National Basic Research Program of China (973 Program)
Basic Research Program of Shenzhen
Russian Science Foundation

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https://doi.org/10.1038/s41598-023-45995-3
Chekol AFufa M(2022)A survey on next location prediction techniques, applications, and challengesEURASIP Journal on Wireless Communications and Networking10.1186/s13638-022-02114-62022:1Online publication date: 31-Mar-2022
https://dl.acm.org/doi/10.1186/s13638-022-02114-6
Qu QZhang SHosseini Bamakan SDoulkeridis CVouros G(2022)Understanding Health Data by Mobility AnalyticsGeospatial Technology for Human Well-Being and Health10.1007/978-3-030-71377-5_5(79-90)Online publication date: 22-Mar-2022
https://doi.org/10.1007/978-3-030-71377-5_5
Alowayr AAlsalooli LAlshahrani AAkaichi J(2021)A Review of Trajectory Data Mining Applications2021 International Conference of Women in Data Science at Taif University (WiDSTaif )10.1109/WiDSTaif52235.2021.9430226(1-6)Online publication date: 30-Mar-2021
https://doi.org/10.1109/WiDSTaif52235.2021.9430226
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