The invention relates to the technical field of trajectory data processing, in particular to a general adjoint mode distributed mining method based on large-scale trajectory data, which comprises thefollowing steps of: 1, establishing a trajectory data set; 2, performing distributed clustering on the trajectory data set: performing density clustering through a DBSCANCD algorithm; 3, enabling theTCB algorithm to take a density clustering result as input, and boundary points are reasonably divided by calculating the similarity among set members; and 4, performing distributed mining on the trajectory data set: performing segmentation and re-division on input of general adjoint mode mining by a GSPR algorithm, and then performing mining by an SAE algorithm. According to the method, the universal accompanying mode can be better mined.

General adjoint mode distributed mining method based on large-scale trajectory data

The invention provides an aero-engine pump dispatching system anomaly detection method based on an improved DBSCAN algorithm. Characteristic parameters are selected according to working characteristics of the aero-engine pump dispatching system, and then characteristic extraction is carried out from parameter data to obtain an input characteristic matrix of the algorithm. Meanwhile, according to the characteristics of input data of the algorithm, an innovative anomaly detection algorithm combining dynamic time warping and a density-based clustering algorithm with noise is provided. Compared with a traditional anomaly detection algorithm based on normal distribution, the anomaly detection result of the algorithm provided by the invention is more accurate. According to the method, the defectthat the density-based clustering algorithm with noise cannot mine the relationship among the time sequences in the aspect of processing the time sequence problem is overcome, and the density-based clustering algorithm with noise is combined with the dynamic time warping, so that the capability of the algorithm for processing the time sequence problem is improved.

Aero-engine pump dispatching system anomaly detection method based on improved DBSCAN algorithm

GPS accuracy can be compromised in urban areas due to multipath issues, leading to low accuracy in GPS location. Therefore, map matching has been introduced as a method to reduce the GPS location error. However, current map matching methods require high-frequency GPS data, and may not perform well with low-frequency data. To address this issue, this study explores the use of machine learning technology and classification methods to adjust the low-frequency GPS location. The model developed in this research employs features such as speed, heading, and location of the previous GPS point for machine learning training, ultimately leading to more accurate map matching for low-frequency GPS data.&#x0D; &#x0D; The objective of this study is to develop a more accurate and efficient map matching method for low-frequency GPS data using machine learning techniques. Experiment results show�that the Random Forest machine learning model produced the highest precision, recall, and F1-Score. Additionally, this model is less sensitive to changes in data frequency. However, the Decision Tree model was the fastest in terms of prediction time and required less time for training than the Random Forest model.&#x0D; &#x0D; In conclusion, this study shows that machine learning technology can be used to improve map matching methods for low-frequency GPS data. The Random Forest model appears to be the most effective in terms of accuracy, while the Decision Tree model is the fastest. These findings can be used to optimize map matching for a wide range of applications, including transportation and navigation. 

pdf/real-time-map-matching-for-low-frequency-gps-data-based-on-3o1pqu4xun.pdf

Real time map matching for low frequency GPS data based on machine learning technology

The invention discloses a meta-learning-based vehicle trajectory clustering method and system, belongs to the technical field of vehicle trajectory clustering, and solves the problem that an optimal clustering result cannot be obtained due to the fact that multiple different types of trajectory data adopt a single trajectory clustering algorithm in the prior art. A meta-learning-based vehicle trajectory clustering method comprises the following steps: collecting different types of GPS vehicle trajectory data, and obtaining an optimal DBSCAN clustering algorithm corresponding to the different types of GPS vehicle trajectory data; collecting GPS vehicle trajectory data, and obtaining a meta-learning device used for vehicle trajectory type division through training; and acquiring a vehicle trajectory type corresponding to the GPS vehicle trajectory data by using the meta-learning device, and clustering the GPS vehicle trajectory data by using an optimal DBSCAN clustering algorithm corresponding to the vehicle trajectory type to obtain a clustering result of the GPS vehicle trajectory data. The optimal clustering result can be obtained for various different types of trajectory data.

Meta-learning-based vehicle trajectory clustering method and system

The insufficient accuracy of GPS technology leads to the sampling trajectory data being away from the actual road. In order to improve the accuracy of GPS trajectory data for matching to map, a map matching integration algorithm based on historical trajectory data is proposed. Firstly projection distance and hidden Markov model are used respectively to compare the matching results. Then the difference road segments are found and the DBSCAN algorithm is used to cluster the historical trajectory data to adjust the segments. Our experiment uses the truck trajectory data to test the algorithm. The results show that the map matching integration algorithm effectively improves the accuracy of map matching.

Map Matching Integration Algorithm Based on Historical Trajectory Data

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Meta-learning-based vehicle trajectory clustering method and system

Map Matching Integration Algorithm Based on Historical Trajectory Data