Road network travel time prediction method based on tensor neural network

Abstract

The invention relates to a road network travel time prediction method based on a tensor neural network. The method comprises the following steps: (1) constructing a road network travel time tensor based on multi-dimensional features; (2) decomposing a road network travel time tensor to obtain a feature matrix of the tensor in each dimension; (3) predicting and obtaining a time sequence characteristic matrix through a long and short time recording neural network; and (4) reconstructing a road network travel time tensor, and obtaining predicted road network travel time stored in a tensor form. According to the tensor neural network provided by the invention, large-scale road network time sequence prediction can be achieved only by predicting the factor matrix of the tensor through a big data compression technology, and the tensor neural network has strong robustness for noise and data loss.

Description

Technical field: [0001] The invention belongs to the field of intelligent transportation, and relates to a road network travel time prediction method, in particular to a road network travel time prediction method based on a tensorized neural network under incomplete data conditions. Background technique: [0002] Road network travel time is an important parameter in intelligent transportation systems. Reasonable and accurate estimation of travel time can effectively identify road network status, and then provide data feedback and theoretical support for traffic decision-making. [0003] Some people in China have proposed travel time prediction methods driven by massive traffic data. However, most of them are model predictions under the scenario where the original data is complete and the data accuracy is high. However, in actual traffic scenarios, due to equipment failure, human factors, weather and environmental influences, etc., it is inevitable that there will be errors o...

AI Technical Summary

Problems solved by technology

[0003] Some people in China have proposed a travel time prediction method driven by massive traffic data, but most of them are model predictions under the scenario where the original data is complete and the data accuracy is high

However, in actual traffic scenarios, due to equipment failure, human factors, weather and environmental influences, etc., it is inevitable that there will be errors or even missing in the original data collection, which directly affects the quality of the original traffic data, and then affects the data-driven The model accuracy of

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