Mining Spatiotemporal Mobility Patterns Using Improved Deep Time Series Clustering

Mining Spatiotemporal Mobility Patterns Using Improved Deep Time Series Clustering

Mining spatiotemporal mobility patterns is crucial for optimizing urban planning, enhancing transportation systems, and improving public safety by providing useful insights into human movement and behavior over space and time. As an unsupervised learning technique, time series clustering has gained...

Full description

Saved in:
Bibliographic Details
Main Authors: Ziyi Zhang, Diya Li, Zhe Zhang, Nick Duffield
Format: Article
Language:English
Published: MDPI AG 2024-10-01
Series:ISPRS International Journal of Geo-Information
Subjects:
spatiotemporal data mining
mobility patterns
time series clustering
deep learning
Online Access:https://www.mdpi.com/2220-9964/13/11/374
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items