Trip Purpose Imputation Using GPS Trajectories with Machine Learning

We studied trip purpose imputation using data mining and machine learning techniques based on a dataset of GPS-based trajectories gathered in Switzerland. With a large number of labeled activities in eight categories, we explored location information using hierarchical clustering and achieved a clas...

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Autores principales:	Qinggang Gao, Joseph Molloy, Kay W. Axhausen
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	class noise data mining ensemble filter hierarchical clustering machine learning random forest Geography (General) G1-922
Acceso en línea:	https://doaj.org/article/5b45681ef4ff4326aacb528c01cb781d
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id	oai:doaj.org-article:5b45681ef4ff4326aacb528c01cb781d
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spelling	oai:doaj.org-article:5b45681ef4ff4326aacb528c01cb781d2021-11-25T17:53:10ZTrip Purpose Imputation Using GPS Trajectories with Machine Learning10.3390/ijgi101107752220-9964https://doaj.org/article/5b45681ef4ff4326aacb528c01cb781d2021-11-01T00:00:00Zhttps://www.mdpi.com/2220-9964/10/11/775https://doaj.org/toc/2220-9964We studied trip purpose imputation using data mining and machine learning techniques based on a dataset of GPS-based trajectories gathered in Switzerland. With a large number of labeled activities in eight categories, we explored location information using hierarchical clustering and achieved a classification accuracy of 86.7% using a random forest approach as a baseline. The contribution of this study is summarized below. Firstly, using information from GPS trajectories exclusively without personal information shows a negligible decrease in accuracy (0.9%), which indicates the good performance of our data mining steps and the wide applicability of our imputation scheme in case of limited information availability. Secondly, the dependence of model performance on the geographical location, the number of participants, and the duration of the survey is investigated to provide a reference when comparing classification accuracy. Furthermore, we show the ensemble filter to be an excellent tool in this research field not only because of the increased accuracy (93.6%), especially for minority classes, but also the reduced uncertainties in blindly trusting the labeling of activities by participants, which is vulnerable to class noise due to the large survey response burden. Finally, the trip purpose derivation accuracy across participants reaches 74.8%, which is significant and suggests the possibility of effectively applying a model trained on GPS trajectories of a small subset of citizens to a larger GPS trajectory sample.Qinggang GaoJoseph MolloyKay W. AxhausenMDPI AGarticleclass noisedata miningensemble filterhierarchical clusteringmachine learningrandom forestGeography (General)G1-922ENISPRS International Journal of Geo-Information, Vol 10, Iss 775, p 775 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	class noise data mining ensemble filter hierarchical clustering machine learning random forest Geography (General) G1-922
spellingShingle	class noise data mining ensemble filter hierarchical clustering machine learning random forest Geography (General) G1-922 Qinggang Gao Joseph Molloy Kay W. Axhausen Trip Purpose Imputation Using GPS Trajectories with Machine Learning
description	We studied trip purpose imputation using data mining and machine learning techniques based on a dataset of GPS-based trajectories gathered in Switzerland. With a large number of labeled activities in eight categories, we explored location information using hierarchical clustering and achieved a classification accuracy of 86.7% using a random forest approach as a baseline. The contribution of this study is summarized below. Firstly, using information from GPS trajectories exclusively without personal information shows a negligible decrease in accuracy (0.9%), which indicates the good performance of our data mining steps and the wide applicability of our imputation scheme in case of limited information availability. Secondly, the dependence of model performance on the geographical location, the number of participants, and the duration of the survey is investigated to provide a reference when comparing classification accuracy. Furthermore, we show the ensemble filter to be an excellent tool in this research field not only because of the increased accuracy (93.6%), especially for minority classes, but also the reduced uncertainties in blindly trusting the labeling of activities by participants, which is vulnerable to class noise due to the large survey response burden. Finally, the trip purpose derivation accuracy across participants reaches 74.8%, which is significant and suggests the possibility of effectively applying a model trained on GPS trajectories of a small subset of citizens to a larger GPS trajectory sample.
format	article
author	Qinggang Gao Joseph Molloy Kay W. Axhausen
author_facet	Qinggang Gao Joseph Molloy Kay W. Axhausen
author_sort	Qinggang Gao
title	Trip Purpose Imputation Using GPS Trajectories with Machine Learning
title_short	Trip Purpose Imputation Using GPS Trajectories with Machine Learning
title_full	Trip Purpose Imputation Using GPS Trajectories with Machine Learning
title_fullStr	Trip Purpose Imputation Using GPS Trajectories with Machine Learning
title_full_unstemmed	Trip Purpose Imputation Using GPS Trajectories with Machine Learning
title_sort	trip purpose imputation using gps trajectories with machine learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5b45681ef4ff4326aacb528c01cb781d
work_keys_str_mv	AT qingganggao trippurposeimputationusinggpstrajectorieswithmachinelearning AT josephmolloy trippurposeimputationusinggpstrajectorieswithmachinelearning AT kaywaxhausen trippurposeimputationusinggpstrajectorieswithmachinelearning
_version_	1718411862635184128

Trip Purpose Imputation Using GPS Trajectories with Machine Learning

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