Human movement mode speculation model based on variation track context perception, training method and speculation method

Abstract

The invention discloses a human movement mode speculation model, a training method and a speculation method based on variation track context perception. The method comprises the following steps: firstly, respectively obtaining a track semantic vector and a variation hidden variable through a circular track encoder and a variation track encoder; and obtaining an attention vector of the track basedon a variational attention mechanism, cascading the attention vector with the variational hidden variable so as to reconstruct input data of a decoder, and finally restoring a previous track and generating a prediction track according to an output semantic vector of the decoder. According to the invention, the frame of the encoder-decoder solves the problem of track context learning. Two sub-tasks, namely track recovery and track prediction, speculated by a human movement mode are completed; not only can the probability density be estimated and the lower limit of the data possibility be optimized, but also the sequence and time characteristics of human mobility can be captured, the problem of track speculation according to track context perception is effectively solved, and the effect is improved for speculation of a human movement mode.

Description

technical field [0001] The invention belongs to the field of deep learning in machine learning, and relates to a human movement pattern estimation technology based on variational trajectory context perception, which mainly utilizes deep learning to perform large-scale analysis on Location-Based Social Networks (LBSN) based on geographic location information. In-depth mining of large-scale trajectory data, mobility semantic learning at the trajectory level to achieve end-to-end prediction, thereby improving the prediction effect of human mobility patterns. Background technique [0002] Over the past decade, interest in mining human mobility patterns for location-based services-based social networks has grown rapidly. The availability of large amounts of LBSN data facilitates research on user behavior and mobility patterns, such as Point of Interest (POI) recommendation, travel planning, and various privacy protection issues, etc. [0003] Most of the existing research focuse...

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Problems solved by technology

[0004] The purpose of the present invention is to solve the Trajectory Context Learning (TCL) problem that has not been formally defined in the existing semantic trajectory mining, thus proposing a method for trajectory context inference according to the semantic dimension of the trajectory, and using The proposed method addresses the problem of human mobility pattern prediction

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