research-article

Traveler's Next Activity Predication with Location-Based Social Network Data

Authors:

Ying ChenAuthors Info & Claims

PredictGIS'19: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility

Pages 15 - 23

https://doi.org/10.1145/3356995.3364540

Published: 05 November 2019 Publication History

Abstract

The rise of technology and the internet provides powerful means for people from all around the world to communicate and connect with one another. Online social network platforms become go-to places for users to express and share their individuality, which includes choice of activities, locations and associated timestamps. In turn, their opinions affect the point of view of others, who are in their online friendship circle. Users' increasing usage of social networks help accumulate massive amount of data that can be further explored. Particularly, this type of data attracts and allows researchers, who are interested in studying and understanding how social factors and previous experience influence user behavior in term of activity-related travel choice. In this paper, the goal is to utilize such rich data sources to build a model that predicts user next activity. Such model contributes a powerful tool for integrating the location prediction with transportation planning and operations process. Besides, it is valuable in commercial applications to create better recommendation system with higher accuracy and ultimately attract more customers to partnering businesses. By studying the dataset, which contains millions of historical check-ins from thousands of users, it is possible to derive information that are useful in predicting user next activity. The proposed approach applies machine learning techniques on the collected features to deliver highly accurate prediction results with fast training and prediction time.

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Cited By

Najjar AMede K(2023)Where You Are Is What You Do: On Inferring Offline Activities From Location Data2023 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW60847.2023.00113(836-843)Online publication date: 4-Dec-2023
https://doi.org/10.1109/ICDMW60847.2023.00113
Ukkusuri SSezaki KYabe TTsubouchi K(2020)PredictGIS 2019 workshop report: Held in conjunction with ACM SIGSPATIAL 2019SIGSPATIAL Special10.1145/3383653.338366611:3(34-37)Online publication date: 13-Feb-2020
https://dl.acm.org/doi/10.1145/3383653.3383666

Index Terms

Traveler's Next Activity Predication with Location-Based Social Network Data

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Published In

cover image ACM Conferences

PredictGIS'19: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility

November 2019

81 pages

ISBN:9781450369640

DOI:10.1145/3356995

Editors:
Satish V. Ukkusuri
Purdue University, IN, USA
,
Kaoru Sezaki
University of Tokyo, Japan
,
Takahiro Yabe
Purdue University, IN, USA
,
Kota Tsubouchi
Yahoo Japan Corporation, Tokyo, Japan

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 November 2019

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University of Washington

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SIGSPATIAL '19

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SIGSPATIAL

SIGSPATIAL '19: 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 5, 2019

IL, Chicago, USA

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Cited By

Najjar AMede K(2023)Where You Are Is What You Do: On Inferring Offline Activities From Location Data2023 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW60847.2023.00113(836-843)Online publication date: 4-Dec-2023
https://doi.org/10.1109/ICDMW60847.2023.00113
Ukkusuri SSezaki KYabe TTsubouchi K(2020)PredictGIS 2019 workshop report: Held in conjunction with ACM SIGSPATIAL 2019SIGSPATIAL Special10.1145/3383653.338366611:3(34-37)Online publication date: 13-Feb-2020
https://dl.acm.org/doi/10.1145/3383653.3383666

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