Road network travel time prediction method based on tensor neural network

A technology of travel time and neural network, applied in the field of prediction of road network travel time based on tensorized neural network, can solve problems affecting the accuracy of data-driven models, affecting the quality of original traffic data, and original data collection errors, etc., to reduce computational complexity Speed, fast convergence speed, and strong robustness

Active Publication Date: 2021-04-30
BEIHANG UNIV +1
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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 equip...

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  • Road network travel time prediction method based on tensor neural network
  • Road network travel time prediction method based on tensor neural network
  • Road network travel time prediction method based on tensor neural network

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[0033] The present invention will be further described below in conjunction with the accompanying drawings and examples, and the contents of the examples are not intended to limit the protection scope of the present invention.

[0034] The invention relates to a road network travel time prediction method based on a tensorized neural network, which is oriented to data missing scenarios, performs dimensionality reduction of space-time features through tensor decomposition, extracts traffic data principal components, realizes traffic data enhancement, and uses cyclic neural network mining The traffic flow implies temporal characteristics, and integrates the temporal and spatial characteristics of traffic flow to realize high-precision prediction of road network travel time.

[0035] Taking the checkpoint data of Ruian City as an example, the tensorized neural network-based road network travel time prediction method of the present invention will be described in detail below.

[00...

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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...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/30G06N3/04G06N3/08G08G1/01
CPCG06Q10/04G06Q50/30G06N3/049G06N3/08G08G1/0125G08G1/0137G06N3/048
Inventor 于海洋张子洋任毅龙卢健刘帅
Owner BEIHANG UNIV
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