A road congestion recognition visualization method based on dbscan+

Abstract

The invention discloses a road congestion identification and visualization method based on DBSCAN+. Firstly, the uploaded data of a large number of floating vehicles OBD vehicle terminals in the city are preprocessed and cleaned; according to the instantaneous speed, the GPS track point data of slow driving within a unit period is extracted; DBSCAN+ algorithm parameters, the extracted data points are clustered in parallel to obtain the initial cluster block in the slow-moving area; by calculating the surface distance between the data points in the cluster block, the two furthest points are found and the line segment is fitted; according to The topological relationship between each road segment in the actual road network will be fitted to the line segment for map matching correction; finally, the driving distance of different vehicles in each type of cluster block within a unit time period is calculated separately, and the average driving distance is comprehensively calculated to judge the congestion level of each type of cluster block , and are visualized in different colors. The invention can adapt to large-scale urban taxi OBD terminal GPS trajectory data, facilitates real-time identification of urban road congestion through taxi running conditions, and has good visualization effect.

Description

technical field [0001] The invention relates to the field of traffic big data, in particular to a road congestion identification and visualization method based on DBSCAN+ (Density-BasedSpatial Clustering of Applications with Noise Plus). Background technique [0002] With the increasingly serious problem of urban traffic congestion, establishing an effective road congestion identification system and accurately identifying traffic congestion sections in cities has become a current research direction. [0003] In the existing traffic jam detection technology, the traffic jam detection methods can be divided into the following categories according to the different processing methods of traffic data and the selected eigenvalues: (1) Based on the vehicle speed: some vehicles are directly detected by vehicle GPS The obtained vehicle speed data can be used directly after processing, or can be calculated from the time and mileage data of the vehicle. Xu L et al. used floating car d...

AI Technical Summary

Problems solved by technology

[0004] These commonly used traffic congestion detection methods are based on the idea of ​​extracting effective data from traffic data processing. In many cases, because traffic congestion detection does not have accurate results as a criterion, research in this field is basically unsupervised learning, and the data cannot be divided. Form a training set and a test set to verify the experimental results, and there are many ways to judge the results, so there is no unified definition of traffic congestion

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