NEAT: Road Network Aware Trajectory Clustering

2012 ◽  
Author(s):  
Binh Han ◽  
Ling Liu ◽  
Edward Omiecinski
Keyword(s):  
Road Network ◽  
Trajectory Clustering

scholarly journals Road-Network Aware Trajectory Clustering: Integrating Locality, Flow, and Density

2015 ◽  
Vol 14 (2) ◽  
pp. 416-429 ◽  
Author(s):  
Binh Han ◽  
Ling Liu ◽  
Edward Omiecinski
Keyword(s):  
Road Network ◽  
Trajectory Clustering

scholarly journals Trajectory Clustering in Road Network Environment

2009 ◽  
Vol 16D (3) ◽  
pp. 317-326
Author(s):  
Ji-Haeng Bak ◽  
Jung-Im Won ◽  
Sang-Wook Kim
Keyword(s):  
Road Network ◽  
Network Environment ◽  
Trajectory Clustering

Segmentation Based Trajectory Clustering in Road Network with Location Sensing Technology

2013 ◽  
Vol 11 (9) ◽  
pp. 1779-1782
Author(s):  
Seokil Song ◽  
Dongho Kwak ◽  
Yunsik Kwak ◽  
Kyoungsoo Bok ◽  
Daesik Ko
Keyword(s):  
Road Network ◽  
Trajectory Clustering ◽  
Sensing Technology ◽  
Location Sensing

Vehicle trajectory-clustering method based on road-network-sensitive features

2021 ◽  
pp. 1-19
Author(s):  
Chuanming Chen ◽  
Zhen Ye ◽  
Fan Hu ◽  
Shan Gong ◽  
Liping Sun ◽  
...  
Keyword(s):  
Public Transportation ◽  
Road Network ◽  
Network Connectivity ◽  
Clustering Methods ◽  
Trajectory Clustering ◽  
Clustering Method ◽  
The Road ◽  
On The Road ◽  
Vehicle Trajectory ◽  
Public Transportation Planning

Existing trajectory-clustering methods do not consider road-network connectivity, road directionality, and real path length while measuring the similarity between different road-network trajectories. This paper proposes a trajectory-clustering method based on road-network-sensitive features, which can solve the problem of similarity metrics among trajectories in the road network, and effectively combine their local and overall similarity features. First, the method performs the primary clustering of trajectories based on the overall vehicle motion trends. Then, the map-matched trajectories are clustered based on the road segment density, connectivity, and corner characteristics. Finally, clustering is then merged based on the multi-area similarity measure. The visualization and experimental results on real road-network trajectories show that the proposed method is more effective and comprehensive than existing methods, and more suitable for urban road planning, public transportation planning, and congested road detection.

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