Moving path hybrid forecasting method oriented to data sparse environment

Abstract

The invention provides a moving path hybrid forecasting method oriented to a data sparse environment. The moving path hybrid forecasting method comprises the steps of: acquiring mobile position data information; processing data , wherein data preprocessing and data semantic analysis are carried out on the data; constructing a semantic knowledge base, wherein original trajectory data is subjected to rich semantic transformation and fusion processing, so as to construct the semantic knowledge base; constructing a hybrid online prediction model which is based on the semantic knowledge base and established on the basis of forward pattern similarity degree matching calculation and a high-order Markov model; and outputting a predicted path, wherein a trajectory fragment to be predicted is input into the hybrid online prediction model for prediction, and the predicted path is output. The moving path hybrid forecasting method effectively overcomes the problem of pattern matching failure caused by data sparse condition, significantly improves the accuracy of path prediction, and satisfies the requirements on real-time performance, high efficiency, predictability and the like of mobile service application.

Description

technical field [0001] The invention belongs to the technical field of mobile computing, and in particular relates to a mixed prediction method for moving paths in a data-sparse environment. Background technique [0002] At present, with the rapid development and widespread popularization of mobile positioning and tracking technology, it is possible to use location-aware devices to obtain historical trajectory data of moving objects. It has become a significant trend and inevitable feature in the field of mobile computing to realize the value extraction and knowledge discovery of historical trajectory big data through semantic modeling and calculation of historical trajectory data, thereby supporting related mobile service applications. attention from academia and industry. [0003] Based on the large-scale group sensing historical trajectory data, the universal and regular mobile characteristics and behavior patterns can be efficiently mined and extracted to build a mobile...

AI Technical Summary

Problems solved by technology

However, in practical applications, the above methods have two problems, specifically as follows: 1) The context and background knowledge data are not easy to obtain, making it difficult to combine Map Matching Technique or Traffic Flow statistical methods to It is difficult to improve the prediction accuracy of moving paths; 2) The data sparsity problem (Data Sparsity Problem) caused by the low value density of perceptual data makes the traditional methods based on approximation calculation or pattern matching difficult to work

The existence of the above problems has brought great challenges to the application services of mobile path prediction

Images