BDCH-DBSCAN-based taxi passenger-carrying hotspot identification method

Abstract

The invention discloses a BDCH-DBSCAN-based taxi passenger-carrying hotspot identification method. The method comprises the steps of firstly providing a large amount of taxi GPS data tracks, and deleting interference data from the data tracks; extracting get-on data points and get-off data points in the GPS data tracks; performing multi-thread block clustering on the extracted data points, separating out core points by adopting a DBSCAN algorithm, obtaining a neighbor node set of the core points, and performing cluster expansion operation on the core points, wherein cluster expansion refers tooperations of performing class cluster division on neighbor points of the core points and adding the neighbor core points with a number greater than a minimum cluster point number and domains of theneighbor core points into clusters; and according to a curved surface distance between two points, finding out neighbor nodes in a scanning radius, finding out a maximum density point in each clusterto serve as a cluster center, and cyclically clustering cluster center sets again until the set quantity and the precision meet the requirements. According to the method, large-scale data can be adapted; class cluster centers are convenient to identify; the multi-thread clustering speed is high; and the accuracy is high.

Description

technical field [0001] The invention relates to the field of traffic big data, in particular to a BDCH-DBSCAN-based identification method for taxi passenger hotspots. Background technique [0002] Nowadays, GPS trajectory data, as a kind of traffic big data, has been widely used. The pick-up and pick-up hotspots formed by a large number of taxi pick-up and pick-up points are of great significance to the analysis of the temporal and spatial distribution of residents' travel and urban traffic planning. [0003] In the prior art, there are mainly two ways to extract hotspots based on taxi passengers: (1) By dividing grid cells: such as the improved DBSCAN algorithm based on road network constraints, and the K-Means traffic hotspots based on grids; The area recognition algorithm uses the data field potential value threshold method to detect the aggregation mode of the trajectory points and extract the hotspot area. (2) Unsupervised clustering methods: such as the DBSCAN algori...

AI Technical Summary

Problems solved by technology

However, this method simply considers the distance between data points and the minimum number of points required for each core point. In the case of a large amount of data, there are often too many trajectory points in the cluster, a wide coverage area, Problems such as poor visualization and hotspots cannot be accurately found

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