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

Abstract

Abstract-Mining trajectory data has been gaining significant interest in recent years. However, existing approaches to trajectory clustering are mainly based on density and Euclidean distance measures. We argue that when the utility of spatial clustering of mobile object trajectories is targeted at road-network aware locationbased applications, density and Euclidean distance are no longer the effective measures. This is because traffic flows in a road network and the flow-based density characterization become important factors for finding interesting trajectory clusters. We propose NEAT a road-network aware approach for fast and effective clustering of trajectories of mobile objects travelling in road networks. Our approach carefully considers the traffic locality characterized by the physical constraints of the road network, the traffic flow among consecutive road segments, and the flow-based density to organize trajectories into spatial clusters in a comprehensive three-phase clustering framework. NEAT discovers spatial clusters as groups of sub-trajectories which describe both dense and highly continuous flows of mobile objects. We perform extensive experiments with mobility traces generated using different scales of real road networks. Experimental results demonstrate the flexibility of the NEAT system and show that NEAT is highly accurate and runs orders of magnitude faster than existing density-based trajectory clustering approaches.

Spatial temporal analysis of vehicle routing problem from online car-hailing trajectories

“…In this study, we group DSi in this case into the same cluster. Then, we improve the clustering method in the third stage of NEAT (road NEtwork Aware Trajectory clustering) proposed by Han et al (2015) to cluster all DS groups and detect the avoided road segments by drivers. Specifically, the improvement mainly includes: 1) the clustering unit is road segment with the same direction of traffic flow; 2) the distance between two clustering unit is calculated by using Hausdorff distance; 3) the threshold of clustering is adaptively acquired based on the input dataset by using the method proposed by Lee et al (2007).…”

Spatial–Temporal Analysis of Vehicle Routing Problem from Online Car-Hailing Trajectories

“…In this study, we group DS i in this case into the same cluster. Then, we improve the clustering method in the third stage of NEAT (road NEtwork Aware Trajectory clustering) proposed by Han et al [12] to cluster all DS groups and detect the road segments avoided by drivers. Specifically, the improvement mainly includes: (1) the clustering unit is a road segment with the same direction of traffic flow; (2) the distance between two clustering units is calculated by using the Hausdorff distance [47]; (3) the threshold of clustering is adaptively acquired based on the input dataset by using the method proposed by Lee et al [48].…”

“…We use the Jaccard index to quantify the similarity between the actual route and the planned route of the same OD pair and visualize the similarity. Next, we employ the Networkaware Trajectory Clustering (NEAT) method [12] to detect high-frequency PRADs by using clustering techniques, and their causes are further analyzed in relation to traffic jams and accidents. By using the massive car-hailing trajectories provided by the DiDi company in the city of Wuhan, we find that about 65% of PRADs correspond to serious traffic jams on workdays.…”

A network distance and graph-partitioning-based clustering method for improving the accuracy of urban hotspot detection

“…Despite a few studies on network-constrained clustering methods of hotspot detection (Steenberghen et al, 2010;El Mahrsi and Rossi, 2012;Han et al, 2015), the related work is still in its early stage in terms of accurate assessment. There is an urgent necessity to investigate a new network-constrained and graph-partitioning-based clustering method to precisely detect hotspots based on linear representation of pick-up or drop-off events extracted from taxi trajectories.…”