A Systematic Approach to Clustering Whole Trajectories of Mobile Objects in Road Networks

Han, Binh; Liu, Ling; Omiecinski, Edward

doi:10.1109/tkde.2017.2652454

Cited by 18 publications

(9 citation statements)

References 22 publications

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“…Graph-based methods, such as Network Hausdorff Distance(NHD) [8], [9], grid with road network information method [26], and road network and feature vector [13], use road network information to simplify the analysis of trajectories. Road network information is similar to the nodes and edges in the traveling salesman problem (TSP).…”

Section: Graph-based Methodsmentioning

confidence: 99%

Common Sub-Trajectory Clustering via Hypercubes in Spatiotemporal Space

Hsu

Lee

2020

IEEE Access

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Conventional sub-trajectory clustering is used to identify similarities among multiple trajectories. Existing methods tend to overlook many of the relevant sub-trajectories; others require a road network as input; all are significantly slowed down considerably by large datasets. In this paper, we propose a novel approach to clustering sub-trajectory in which trajectories are transformed into a set of Hypercubes. The Hypercubes are pairwise-matched to find an intersection and then clustered accordingly. The performance of the proposed scheme was compared with that of grid clustering (i.e., constant time technique) in terms of memory usage, computational speed, and compared with a state-of-art method, TraClus, by assessing their accuracy. The experiment results show that Hypercube clustering can identify common sub-trajectories more swiftly and with less memory usage than grid clustering. The accuracy of Hypercube clustering is superior to TraClus. INDEX TERMS Urban computing, similar trajectories, ridesharing paths, common sub-trajectories clustering.

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Section: Graph-based Methodsmentioning

confidence: 99%

Common Sub-Trajectory Clustering via Hypercubes in Spatiotemporal Space

Hsu

Lee

2020

IEEE Access

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“…Clustering analysis based on position points [28][29][30][31][32][33][34][35][36] is generally used to find interesting points that are nearby or hot spots where people like to gather on weekends and holidays [36]. Location-based clustering methods include K-means clustering, trajectory clustering based on traffic density, fuzzy clustering, etc.…”

Section: Related Workmentioning

confidence: 99%

Taxi Passenger Hot Spot Mining Based on a Refined K-Means++ Algorithm

Wang

Ren

2021

IEEE Access

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With the development of information technology, it is possible to explore the spatialtemporal distribution characteristics of taxi travel demand by examining taxi GPS location data in order to master the actual supply and demand levels of different hot spots at different time periods. At present, in hot spot mining, the existing research on the clustering of passenger hot spots has some performance problems, such as insufficient clustering accuracy and high algorithm time complexity. The purpose of this paper is to propose a two-level subdivision concept and improve the K-means++ algorithm to finish the fine clustering of hot spots of taxi passengers. The first-level subdivision establishes a dynamic adjustable region with time and geographical range. In the second layer, a Gaussian mixture model is used for the data distribution statistics, and the optimal subdivision area number is determined according to the minimum principle of the Akaike information criterion and Bayesian information criterion. The SSE (sum of the square distance errors) is used to determine the optimal cluster number k for each local area. Finally, the K-means++ algorithm is used to complete the clustering of each local area. A week of green taxi data from New York City was used to validate the method and compare it to the traditional K-means and DBSCAN approaches. The proposed method achieved better accuracy with comparable time consumption. This demonstrated the value of the approach for hot spot data mining although clustering still has some important advantages. In addition, the hot spots in the morning peak and weekend are displayed visually, which is helpful to provide the guidance for urban transportation and planning.

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“…However, the automatic encoding of trajectories by deep learning belongs to supervised learning, and it is difficult to be widely used in trajectory data lacking label information. Han et al [9] proposed the whole trajectory algorithm TRACEMOB, which uses the coincidence rate of the trajectory in the grid as the basis for the trajectory similarity and converts the distance in the grid space into a d-dimensional Euclidean space. Finally, the K-means-based algorithm is used to complete the clustering.…”

Section: Related Workmentioning

confidence: 99%

“…Then, the clusters are adjusted and updated based on the iterative process, and the clustering centers are updated after each iteration. If the conditions are met, the clustering process will stop, and the trajectory clusters and abnormal trajectory set will be output (lines [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Specifically, according to the outlier function, it is determined whether each trajectory can be clustered into a cluster or temporarily as an exception (lines [4][5][6][7][8][9][10][11][12].…”

Section: The Adjustment and Update Of Clustersmentioning

confidence: 99%

“…In both figures, the same color trajectories are in the same cluster. As can be seen from Figures 1(b) and 1(c), these 9 trajectories are clustered by the classic subtrajectory clustering algorithms TraClus [8] and the classic whole trajectory clustering algorithm GridCSD-TraceMob (Han et al [9]), and they are partitioned into 4 clusters. In Figure 1(b), those marked black squares in some trajectories are the turning points where the trajectories are divided into a series of subtrajectories based on angular offset of adjacent trajectory points and road segments by the algorithm TraClus.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Grid‐Based Whole Trajectory Clustering in Road Networks Environment

Wang

Niu

et al. 2021

Wireless Communications and Mobile Computing

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In the data mining of road networks, trajectory clustering of moving objects plays an important role in many applications. Most existing algorithms for this problem are based on every position point in a trajectory and face a significant challenge in dealing with complex and length-varying trajectories. This paper proposes a grid-based whole trajectory clustering model (GBWTC) in road networks, which regards the trajectory as a whole. In this model, we first propose a trajectory mapping algorithm based on grid estimation, which transforms the trajectories in road network space into grid sequences in grid space and forms grid trajectories by recognizing and eliminating redundant, abnormal, and stranded information of grid sequences. We then design an algorithm to extract initial clustering centers based on density weight and improve a shape similarity measuring algorithm to measure the distance between two grid trajectories. Finally, we dynamically allocate every grid trajectory to the best clusters by the nearest neighbor principle and an outlier function. For the evaluation of clustering performance, we establish a clustering criterion based on the classical Silhouette Coefficient to maximize intercluster separation and intracluster homogeneity. The clustering accuracy and performance superiority of the proposed algorithm are illustrated on a real-world dataset in comparison with existing algorithms.

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A Systematic Approach to Clustering Whole Trajectories of Mobile Objects in Road Networks

Cited by 18 publications

References 22 publications

Common Sub-Trajectory Clustering via Hypercubes in Spatiotemporal Space

Common Sub-Trajectory Clustering via Hypercubes in Spatiotemporal Space

Taxi Passenger Hot Spot Mining Based on a Refined K-Means++ Algorithm

Grid‐Based Whole Trajectory Clustering in Road Networks Environment

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