Method for predicting path destination of moving object

Abstract

The invention discloses a method for predicting the path destination of a moving object, and mainly solves the problems of sparse data, a low hit rate and poor real-time performance during the prediction of the path destination in the prior art. The method includes the following steps: off-line calculating the history path data in a training data set to obtain a location set, a prior probability of each location, a single-step transition probability matrix and a comprehensive transition probability matrix; calculating the conditional probability and the posterior probability of each location being the destination based on the off-line calculated data; on-line predicting the destination of the path to be predicted based on the posterior probability. Compared with the existing path prediction method, the method provided by the invention has the advantages that a prediction can be performed even when the data in the training data set is sparse; the calculation process of the prediction is optimized, so that the impact of the non-aftereffect property of Markov chain on the prediction accuracy is reduced; the prediction accuracy is improved; the method can be used for pushing location-related targeted advertisements and deploying criminal arrest plans in advance.

Description

technical field [0001] The invention belongs to the field of computers, and relates to data mining and location prediction, in particular to a method for predicting the end point of the trajectory of a moving object, that is, by analyzing and mining the obtained historical trajectory data, the destination of the new trajectory is predicted The method can be used for location-related targeted advertisement push, early deployment of criminal arrest plan, etc. Background technique [0002] With the development of Internet of Things technology, various sensors are embedded in portable mobile terminals or fixedly installed on public facilities to participate in people's daily activities. Examples include GPS sensors in taxis and smartphones, among others. They can capture and record changes in people's daily life in real time. Analyze and mine the historical trajectory data collected by these sensors to build a destination prediction model, which is conducive to scientific deci...

AI Technical Summary

Problems solved by technology

[0010] The technical problem to be solved by the present invention is to provide a new method for predicting the end point of the trajectory of the moving object in view of the above-mentioned existing methods, so as to solve the problem of data sparseness easily encountered by using the Bayesian prediction model, and at the same time reduce the calculation of the Markov model. time, improving the accuracy of destination predictions

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