Trip Purpose Imputation Using GPS Trajectories with Machine Learning

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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Bibliographic Details
Main Authors: Qinggang Gao, Joseph Molloy, Kay W. Axhausen
Format: Article
Language:English
Published: MDPI AG 2021-11-01
Series:ISPRS International Journal of Geo-Information
Subjects:
class noise
data mining
ensemble filter
hierarchical clustering
machine learning
random forest
Online Access:https://www.mdpi.com/2220-9964/10/11/775

Internet

https://www.mdpi.com/2220-9964/10/11/775

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