Optimal waiting point recommendation method and system for moving track big data

Abstract

The invention discloses a moving trajectory big data-oriented optimal waiting point recommendation method and system. The method comprises the following steps: S1, obtaining moving trajectory data ofa vehicle; S2, preprocessing the movement track data to obtain vehicle getting-on and getting-off hotspot data; S3, constructing a parallel SP-DBSCAN algorithm according to the get-on and get-off hotspot data; S4, performing clustering analysis by using an SP-DBSCAN algorithm to obtain a plurality of waiting point recommendation areas; S5, according to the plurality of waiting point recommendationareas, obtaining a plurality of centroids of each waiting point recommendation area; S6, recommending an optimal waiting point according to the plurality of centroids and the positions of the passengers, constructing a parallel SP-DBSCAN algorithm, performing clustering analysis by using the SP-DBSCAN algorithm to obtain a waiting point recommendation area, and obtaining the optimal waiting pointaccording to the waiting point recommendation area; therefore, the technical problems of distributed storage and parallel computing of optimal waiting point recommendation based on moving track big data are solved, and the efficiency of processing large-scale moving track data is high.

Description

technical field [0001] The invention relates to the field of recommendation of waiting positions for big data of moving trajectories, in particular to a method and system for recommending optimal waiting points for big data of moving trajectories. Background technique [0002] With the rapid development of data technology (DT, Data Technology), big data mining and analysis of mobile trajectory has become a new concept and new practice to solve urban problems such as traffic congestion. Harmonious development, improve the livability of the city, and provide comprehensive decision-making based on data evidence for the government to implement intelligent transportation precision management. However, with the exponential growth of traffic big data, traditional methods and technologies can no longer meet the storage and computing needs of large-scale traffic data. In recent years, parallel distributed computing has provided a new way for in-depth mining and efficient analysis of...

AI Technical Summary

Problems solved by technology

[0003] The traditional optimal waiting point recommendation method does not take into account the characteristics of passenger mobility, especially with the explosive growth of traffic big data, existing serial algorithms have problems when recommending optimal waiting points based on traditional stand-alone centralized mining platforms. The technical problems of "high memory consumption and I / O overhead, low processing efficiency, and poor scalability" cannot effectively solve the technical problems of distributed storage and parallel computing based on the best waiting point recommendation based on moving trajectory big data

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