Label-Based Trajectory Clustering in Complex Road Networks

Niu, Xinjian; Chen, Ting; Wu, Chase Q.; Niu, Jiajun; Li, Yuran

doi:10.1109/tits.2019.2937882

Cited by 24 publications

(12 citation statements)

References 38 publications

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“…Among the distance-based methods, Euclidean distance is a popular metric to measure the similarity of trajectories [31], but the method incurs a huge computational overhead because of the need to calculate the distance for each pair of trajectories [32]. Similarly, the identifier-based approach incurs additional communication and computational overhead [33]. The mapbased approach achieves trajectory clustering by matching the baseline map with the intermediate representation of the trajectory, but it cannot achieve high precision clustering well due to the complexity of the road map [34].…”

Section: B Applications Of Floating Vehicle Trajectory Data In Remote Sensingmentioning

confidence: 99%

Road Extraction Using a Dual Attention Dilated-LinkNet Based on Satellite Images and Floating Vehicle Trajectory Data

Gao¹,

Wang²,

Wang³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Automatic extraction of road from multi-source remote sensing data has always been a challenging task. Factors such as shadow occlusion and multi-source data alignment errors prevent current deep learning-based road extraction methods from acquiring road features with high complementarity, redundancy, and crossover. Unlike previous works that capture contexts by multi-scale feature fusion, we propose a dual attention dilated-LinkNet (DAD-LinkNet) to adaptively integrate local road features with their global dependencies by joint using satellite image and floating vehicle trajectory data. Firstly, a joint leastsquares feature matching-based floating vehicle trajectory correction model is used to correct the floating vehicle trajectory; then a convolutional network model DAD-LinkNet based on a dual-attention mechanism is proposed, and road features are extracted from the channel domain and spatial domain of the target image in turn by constructing a dual-attention module in the dilated convolutional layer and adopting a cascade connection; a weighted hyperparameter loss function is used as the loss function of the model; finally, the road extraction is completed based on the proposed DAD-LinkNet model. Experiments on three datasets show that the proposed DAD-LinkNet model outperforms the state-of-the-art methods in terms of accuracy and connectivity.

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Section: B Applications Of Floating Vehicle Trajectory Data In Remote Sensingmentioning

confidence: 99%

Road Extraction Using a Dual Attention Dilated-LinkNet Based on Satellite Images and Floating Vehicle Trajectory Data

Gao¹,

Wang²,

Wang³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Each node label is propagated to neighboring nodes according to their similarity. At each step of node propagation, the node updates itself A trajectory clustering algorithm based on an improved Label Propagation algorithm was proposed where road network is modeled as a dual graph to capture and characterize the similarity between nodes [10]. Liu and Guo proposed a semantic trajectory clustering algorithm based on community detection [32], where different community detection algorithms were discussed.…”

Section: Community Detection In Networkmentioning

confidence: 99%

“…Step 2. Each node is traversed in a random order, and each point is assigned to the adjacent module that gives the largest decrease in Equation (10).…”

Section: Trajectory Clusteringmentioning

confidence: 99%

“…The process of trajectory data analysis mainly consists of obtaining and preprocessing trajectory data, trajectory data management, and a variety of mining tasks, including trajectory pattern mining, privacy protection, outlier detection [8,9], and clustering trajectories on complex road networks [10,11]. Many studies have been published, and trajectory data analysis is a very active research field.…”

Section: Introductionmentioning

confidence: 99%

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An Approach to Spatiotemporal Trajectory Clustering Based on Community Detection

Wang

Niu

Zhu

et al. 2021

Wireless Communications and Mobile Computing

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Nowadays, large volumes of multimodal data have been collected for analysis. An important type of data is trajectory data, which contains both time and space information. Trajectory analysis and clustering are essential to learn the pattern of moving objects. Computing trajectory similarity is a key aspect of trajectory analysis, but it is very time consuming. To address this issue, this paper presents an improved branch and bound strategy based on time slice segmentation, which reduces the time to obtain the similarity matrix by decreasing the number of distance calculations required to compute similarity. Then, the similarity matrix is transformed into a trajectory graph and a community detection algorithm is applied on it for clustering. Extensive experiments were done to compare the proposed algorithms with existing similarity measures and clustering algorithms. Results show that the proposed method can effectively mine the trajectory cluster information from the spatiotemporal trajectories.

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“…The massive position and movement information of moving objects are generated continuously, forming large-scale trajectory data. It is of great academic significance and commercial value to mine the underlying distribution information and the evolvement rules, such as urban function partition (Niu et al [1]), traffic jam prediction (Yu et al [2]), and privacy protection (Wang et al [3]). In addition, some classical trajectory clustering algorithms such as DBSCAN (Ester et al [4]) are widely used in anomaly trajectory detection and anomaly event preven-tion (Belhadi et al [5], Belhadi et al [6], Djenouri et al [7]).…”

Section: Introductionmentioning

confidence: 99%

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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Label-Based Trajectory Clustering in Complex Road Networks

Cited by 24 publications

References 38 publications

Road Extraction Using a Dual Attention Dilated-LinkNet Based on Satellite Images and Floating Vehicle Trajectory Data

Road Extraction Using a Dual Attention Dilated-LinkNet Based on Satellite Images and Floating Vehicle Trajectory Data

An Approach to Spatiotemporal Trajectory Clustering Based on Community Detection

Grid‐Based Whole Trajectory Clustering in Road Networks Environment

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