Tensor trajectory path planning method based on context

Abstract

The invention discloses a tensor trajectory path planning method based on context. The invention discloses a tensor trajectory path planning method based on context. The method comprises the steps oftensor modeling; the obtained GPS path data are processed, and a third-order tensor is obtained through tensioning, and the three dimensions represent a driver, a road segment and a time period respectively; Based on known GPS path data, extracting track data in L recent time periods and expanding the track data into a third-order tensor; Each value in the tensor s shown in the specification, andthe tensor is shown in the specification, wherein the tensor is shown in the specification; wherein in the kth time period, the travel time spent by the ith driver on the jth road is a, and L is thelast time period of statistics. The method has the beneficial effects that for Tucker decomposition, the algorithm of the method can be improved; An initial iteration value of an iteration algorithmof the system is randomly initialized, and a tensor unknown solution position is filled with a 0 value and then decomposed. And the value iterated by using the context-aware tensor method is used as the initialization of the algorithm.

Description

technical field [0001] The invention relates to the field of path planning, in particular to a context-based tensor trajectory path planning method. Background technique [0002] As the population increases, travel issues are gaining more and more attention. The development of science and technology has promoted the development of tourism. More and more people are willing to enjoy life. However, enjoying life means making the most of your time and money. A good travel time prediction can not only provide individual users with money-saving, labor-saving and time-saving travel options, but also provide basic traffic management information such as traffic management departments and other institutions, and avoid various traffic congestion through travel time prediction. [0003] In intelligent transportation systems, without a doubt, one of the most important fundamental components is travel time prediction. In recent years, there have been many methods for estimating travel...

AI Technical Summary

Problems solved by technology

In these algorithms, the initial filling of 0 for the unknown position is to ensure that each position in the tensor has a value, but I consider filling in 0 is only the simplest preprocessing method, and I think the direct filling of 0 for the unknown position It may cause some errors in the results; in addition, the existing method that can get the best results, the selected context tensor decomposition, and adding the similarity matrix between roads and space-time to Tucker the tensor Decomposition, this method is an iterative tensor decomposition algorithm, the initial matrix selected is a randomly initialized matrix, and this initial method is not the best initial method

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