Method for identifying traveling OD nodes and extracting path between nodes in big data environment

Abstract

The invention provides a method for identifying traveling OD nodes and extracting a path between nodes in a big data environment. According to the method, traveling path data of massive individuals ismined by using spatial activity data sets of individuals of mobile terminals in a specified time range, and fitting interpolation is performed on the traveling path data, so as to acquire an individual traveling time-space sequence of an equal time interval; a possible cluster region is searched in the individual traveling time-space sequence through a spatial clustering method, intersection angle differences between a center point of the cluster region and external nodes of the cluster region are compared so as to determine whether an extracted cluster point is an OD point, and the travelingtime-space sequence of a user is split. Through adoption of the method, the traveling time-space sequence of massive individuals in a specified time range can be acquired conveniently and automatically at low cost, node regions with an OD feature can be found rapidly through a spatial clustering algorithm and a weighted averaging method, and OD points are determined according to rules, so that ODnode-based road section segmentation is performed on the traveling time-space sequence of the user conveniently and efficiently.

Description

technical field [0001] The invention relates to a method for identifying travel OD nodes and extracting paths between OD nodes based on massive anonymous encrypted time series positioning data in a big data environment, and belongs to the technical field of big data analysis. Background technique [0002] In recent years, with the development of information technology, the amount of data information has shown explosive growth, more and more data sources, and the amount of data has become larger and larger. Among them, data recorded by information sensors such as mobile phones, WIFI, and the Internet of Things has become the most important data source in big data analysis, and its relatively complete individual travel records provide good data for big data, especially traffic big data analysis. support. Taking mobile phones as an example, by 2015, the number of mobile phone users reached 1.306 billion, accounting for more than 96% of the total population. The signal informat...

AI Technical Summary

Problems solved by technology

[0003] However, currently available mobile phone signaling big data only includes anonymous encrypted communication records between users and base stations, among which only communication time and base station numbers are related to user travel behaviors, and user travel behaviors (including travel start and end points, Stopping places, travel routes, travel modes, etc.) are only contained in the mobile phone signaling and are not directly expressed. This requires an efficient and concise algorithm to process the user's travel trajectory data composed of mobile phone signaling data. Identify the O-D point of the user's travel, segment the O-D path of the user's travel, and extract the user's travel behavior characteristics

In the prior art, there is no such algorithm

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