Proposal for a Pivot-Based Vehicle Trajectory Clustering Method

Given the large volume of georeferenced information generated and stored by many types of devices, the study and improvement of techniques capable of operating with these data is an area of great interest. The analysis of vehicular trajectories with the aim of forming clusters and identifying emerging patterns is very useful for characterizing and analyzing transportation flows in cities. This paper presents a new trajectory clustering method capable of identifying clusters of vehicular sub-trajectories in various sectors of a city. The proposed method is based on the use of an auxiliary structure to determine the correct location of the centroid of each group or set of sub-trajectories along the adaptive process. The proposed method was applied on three real databases, as well as being compared with other relevant methods, achieving satisfactory results and showing good cluster quality according to the Silhouette index.

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Segmentasi Buah Mangga Menggunakan MLE dan GMM Sebagai Klasterisasi Pixel

Jurnal Algoritme ◽

10.35957/algoritme.v1i1.435 ◽

2020 ◽

Vol 1 (1) ◽

pp. 57-67

Author(s):

Steven Pranata ◽

Derry Alamsyah

Keyword(s):

Maximum Likelihood ◽

Maximum Likelihood Estimation ◽

Error Rate ◽

Data Distribution ◽

Likelihood Estimation ◽

Clustering Method ◽

Number Of Clusters ◽

Silhouette Coefficient ◽

Good Cluster ◽

Cluster Quality

Segmentation divides an image into parts or segments that are simpler and more meaningful so they can be analyzed further. The solution that has been found is using the Maximum Likelihood Estimation (MLE) method and the Gausian Mixture Model. GMM is a clustering method. GMM is a function consisting of several Gaussian, each identified by k ∈ {1, ..., K}, where K is the number of clusters in our dataset. Maximum Likelihood estimation is a technique used to find a certain point to maximize a function, this technique is very widely used in estimating a data distribution parameter. Tests carried out using mango images with 10 different backgrounds. GMM will cluster the pixels of the mango image to produce averages and covariates. Then the average and covariance will be used by MLE to qualify each pixel of the mango image. In this study GMM and MLE tests were carried out to segment mangoes. Based on the results obtained, the GMM and MLE methods have an error rate of 13.07% for 3 clusters, 8.06% for 4 clusters, and 6.63% for 5 clusters and good cluster quality with silhouette coefficient values of 0.37686 for 3 clusters, 0.29577 for 4 clusters, and 0.26162 for 5 clusters.

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

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-211270 ◽

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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Vehicle trajectory clustering based on 3D information via a coarse-to-fine strategy

Soft Computing ◽

10.1007/s00500-017-2831-0 ◽

2017 ◽

Vol 22 (5) ◽

pp. 1433-1444 ◽

Cited By ~ 5

Author(s):

Huansheng Song ◽

Xuan Wang ◽

Cui Hua ◽

Weixing Wang ◽

Qi Guan ◽

...

Keyword(s):

Trajectory Clustering ◽

Vehicle Trajectory ◽

3D Information ◽

Coarse To Fine

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Data-Driven Method for the Prediction of Estimated Time of Arrival

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211033295 ◽

2021 ◽

pp. 036119812110332

Author(s):

Xuhao Gui ◽

Junfeng Zhang ◽

Zihan Peng ◽

Chunwei Yang

Keyword(s):

Pattern Classification ◽

Trajectory Optimization ◽

Time Estimation ◽

Flight Time ◽

Classification Model ◽

Time Of Arrival ◽

Trajectory Clustering ◽

Clustering Method ◽

Arrival Pattern ◽

Multiple Stages

Predicting the estimated time of arrival (ETA) plays an essential role in decision support (conflict detection, arrival sequencing, or trajectory optimization) for air traffic controllers. In this paper, a new multiple stages strategy for ETA prediction is proposed based on radar trajectories, including arrival pattern identification, arrival pattern classification, and flight time estimation. First, an intention-oriented trajectory clustering method is developed based on a new trajectory representation technique. Such a proposed trajectory clustering method can group trajectories into different arrival patterns in an efficient way. Second, an arrival pattern classification model is constructed based on random forest and XGBoost algorithms. Then, a flight time regression model is trained for each arrival pattern by using the XGBoost algorithm. Information on current states, historical states, and traffic situations is considered to build the feature set during these processes. Finally, the arrival operation toward Guangzhou International Airport is chosen as a case study. The results illustrate that the proposed method and feature engineering approach could improve the performance of ETA prediction. The proposed multiple stages strategy is superior to the single-model-based ETA prediction.

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GPS trajectory clustering method for decision making on intelligent transportation systems

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-179644 ◽

2020 ◽

Vol 38 (5) ◽

pp. 5529-5535

Author(s):

Gary Reyes ◽

Laura Lanzarini ◽

Waldo Hasperué ◽

Aurelio F. Bariviera

Keyword(s):

Decision Making ◽

Intelligent Transportation Systems ◽

Intelligent Transportation ◽

Transportation Systems ◽

Trajectory Clustering ◽

Clustering Method ◽

Gps Trajectory

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Uncertainty Quantification of Trajectory Clustering Applied to Ocean Ensemble Forecasts

Fluids ◽

10.3390/fluids5040184 ◽

2020 ◽

Vol 5 (4) ◽

pp. 184

Author(s):

Guilherme S. Vieira ◽

Irina I. Rypina ◽

Michael R. Allshouse

Keyword(s):

Spectral Clustering ◽

Trajectory Clustering ◽

Clustering Method ◽

Material Transport ◽

Ensemble Forecasts ◽

Uncertainty Model ◽

Ocean Flows ◽

Ocean Forecasting ◽

High Uncertainty ◽

Spectral Clustering Method

Partitioning ocean flows into regions dynamically distinct from their surroundings based on material transport can assist search-and-rescue planning by reducing the search domain. The spectral clustering method partitions the domain by identifying fluid particle trajectories that are similar. The partitioning validity depends on the accuracy of the ocean forecasting, which is subject to several sources of uncertainty: model initialization, limited knowledge of the physical processes, boundary conditions, and forcing terms. Instead of a single model output, multiple realizations are produced spanning a range of potential outcomes, and trajectory clustering is used to identify robust features and quantify the uncertainty of the ensemble-averaged results. First, ensemble statistics are used to investigate the cluster sensitivity to the spectral clustering method free-parameters and the forecast parameters for the analytic Bickley jet, a geostrophic flow model. Then, we analyze an operational coastal ocean ensemble forecast and compare the clustering results to drifter trajectories south of Martha’s Vineyard. This approach identifies regions of low uncertainty where drifters released within a cluster predominantly remain there throughout the window of analysis. Drifters released in regions of high uncertainty tend to either enter neighboring clusters or deviate from all predicted outcomes.

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