HOTSPOTS DETECTION FROM TRAJECTORY DATA BASED ON SPATIOTEMPORAL DATA FIELD CLUSTERING

9 citations as recorded by crossref.

1. Identifying Urban Traveling Hotspots Using an Interaction-Based Spatio-Temporal Data Field and Trajectory Data: A Case Study within the Sixth Ring Road of Beijing D. Yi et al. https://doi.org/10.3390/su12229662
2. Identify and Delimitate Urban Hotspot Areas Using a Network-Based Spatiotemporal Field Clustering Method Z. Xia et al. https://doi.org/10.3390/ijgi8080344
3. Investigation on the Status Quo of Shared Bicycles in Wenzhou and Analysis of Travel Characteristics Y. Zhang https://doi.org/10.54097/ajst.v1i3.454
4. Analysis of spatio-temporal distribution characteristics of passenger travel behaviour based on online ride-sharing trajectory data X. Dong et al. https://doi.org/10.1088/1742-6596/1187/5/052055
5. Travel Time Variability in Urban Mobility: Exploring Transportation System Reliability Performance Using Ridesharing Data Y. Sun & Y. Chen https://doi.org/10.3390/su16188103
6. An Urban Community Discovery Method from a Line Graph Perspective Based on Trajectory Data C. Wang et al. https://doi.org/10.1007/s41651-025-00241-4
7. A Trajectory Clustering Method Based on Moving Index Analysis and Modeling Y. Yang et al. https://doi.org/10.1109/ACCESS.2022.3168993
8. A Two-Phase Clustering Approach for Urban Hotspot Detection With Spatiotemporal and Network Constraints F. Li et al. https://doi.org/10.1109/JSTARS.2021.3068308
9. Discovery of arbitrarily shaped significant clusters in spatial point data with noise J. Huang & J. Tang https://doi.org/10.1016/j.asoc.2021.107452