A Segment-Based Trajectory Similarity Measure in the Urban Transportation Systems

Mao, Yingchi; Zhong, Haishi; Xiao, Xuan; Li, Xiaofang

doi:10.3390/s17030524

Cited by 36 publications

(15 citation statements)

References 18 publications

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“…In recent years, clustering methods have attracted increasing attention of researchers in the fields of data mining and pattern recognition. Up to now, the current clustering methods could be roughly divided into five categories, i.e., partitioning methods [ 36 , 37 ] (e.g., K-means, K-mediods), hierarchical methods (e.g., BIRCH), density-based methods (e.g., DBSCAN [ 38 ]), grid-based methods [ 39 ] (e.g., STING [ 40 ]), and model-based clustering methods [ 41 ].…”

Section: Literature Review Of Clustering Methodsmentioning

confidence: 99%

A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis

Liu

et al. 2017

Sensors

162

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The Shipboard Automatic Identification System (AIS) is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW), a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA) is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our proposed method with traditional spectral clustering and fast affinity propagation clustering. Experimental results have illustrated its superior performance in terms of quantitative and qualitative evaluations.

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Section: Literature Review Of Clustering Methodsmentioning

confidence: 99%

A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis

Liu

et al. 2017

Sensors

162

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“…The semantic graph is defined as G=false(V,E,Dfalse), where the set of nodes are sensors V=S, E is the edge set E∈V⊗V, and D is the set of functional similarity on all of the edges. Dynamic time warping (DTW) method [39] is applied to measure the functional similarity φi,j between sensor si and sensor sj as follows:φi,j=expfalse(−γ×DTWfalse(si,sjfalse)false), where γ is the decay parameter and DTWfalse(si,sjfalse) is the dynamic time warping distance between the data distribution of two sensors si and sensor sj. In the dam safety monitoring systems, we use the average seasonal deformation time series as the dam deformation patterns.…”

Section: Dnn-based Multiple View Learning Frameworkmentioning

confidence: 99%

DNN-MVL: DNN-Multi-View-Learning-Based Recover Block Missing Data in a Dam Safety Monitoring System

Mao

Zhang

et al. 2019

Sensors

Self Cite

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Many sensor nodes have been widely deployed in the physical world to gather various environmental information, such as water quality, earthquake, and huge dam safety. Due to the limitation in the batter power, memory, and computational capacity, missing data can occur at arbitrary sensor nodes and time slots. In extreme situations, some sensors may lose readings at consecutive time slots. The successive missing data takes the side effects on the accuracy of real-time monitoring as well as the performance on the data analysis in the wireless sensor networks. Unfortunately, existing solutions to the missing data filling cannot well uncover the complex non-linear spatial and temporal relations. To address these problems, a DNN (Deep Neural Network) multi-view learning method (DNN-MVL) is proposed to fill the successive missing readings. DNN-MVL mainly considers five views: global spatial view, global temporal view, local spatial view, local temporal view, and semantic view. These five views are modeled with inverse distance of weight interpolation, bidirectional simple exponential smoothing, user-based collaborative filtering, mass diffusion-based collaborative filtering with the bipartite graph, and structural embedding, respectively. The results of the five views are aggregated to a final value in a multi-view learning algorithm with DNN model to obtain the final filling readings. Experiments on large-scale real dam deformation data demonstrate that DNN-MVL has a mean absolute error about 6.5%, and mean relative error 21.4%, and mean square error 8.17% for dam deformation data, outperforming all of the baseline methods.

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“…To measure the distance between the road trajectory and the trajectory in space, Mao et al adopted the point-segment distance, predicted the distance, and measured the segment-segment distances; this approach improved trajectory similarity. However, the algorithm was inaccurate, highly sensitive to sampling methods, exhibited low robustness to noisy data, and was computationally intensive [32]. Wang et al and Wu et al used the orthophoto distance to measure line-to-line distance [33,34].…”

Section: Spatial Metrics Of Trajectory Similaritymentioning

confidence: 99%

Generation of Lane-Level Road Networks Based on a Trajectory-Similarity-Join Pruning Strategy

Zheng

Song²,

et al. 2019

IJGI

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With the development of autonomous driving, lane-level maps have attracted significant attention. Since the lane-level road network is an important part of the lane-level map, the efficient, low-cost, and automatic generation of lane-level road networks has become increasingly important. We propose a new method here that generates lane-level road networks using only position information based on an autonomous vehicle and the existing lane-level road networks from the existing road-level professionally surveyed without lane details. This method uses the parallel relationship between the centerline of a lane and the centerline of the corresponding segment. Since the direct point-by-point computation is huge, we propose a method based on a trajectory-similarity-join pruning strategy (TSJ-PS). This method uses a filter-and-verify search framework. First, it performs quick segmentation based on the minimum distance and then uses the similarity of two trajectories to prune the trajectory similarity join. Next, it calculates the centerline trajectory for lanes using the simulation transformation model by the unpruned trajectory points. Finally, we demonstrate the efficiency of the algorithm and generate a lane-level road network via experiments on a real road.

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A Segment-Based Trajectory Similarity Measure in the Urban Transportation Systems

Cited by 36 publications

References 18 publications

A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis

A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis

DNN-MVL: DNN-Multi-View-Learning-Based Recover Block Missing Data in a Dam Safety Monitoring System

Generation of Lane-Level Road Networks Based on a Trajectory-Similarity-Join Pruning Strategy

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