A new method of data missing estimation with FNN-based tensor heterogeneous ensemble learning for internet of vehicle

The Internet of Vehicles (IoV) can obtain traffic information through a large number of data collected by sensors. However, the lack of data, abnormal data, and other low-quality problems have seriously restricted the development and application of the IoV. To solve the problem of missing data in a largescale road network, the previous research achievements show that tensor decomposition method has the advantages in solving multi-dimensional data imputation problems, so we adopt this tensor mode to model traffic velocity data. A new method of data missing estimation with tensor heterogeneous ensemble learning based on FNN (Fuzzy Neural Network) named FNNTEL is proposed in this paper. The performance of this method is evaluated by our experiments and analysis. The proposed method is applied to be tested by the real data captured in Guangzhou and Tianjin of China respectively. A large number of experimental tests show that the performance of the new method is better than other commonly used technologies and different missing data generation models. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

The Internet of Vehicles (IoV) collects traffic information through sensors, but issues like missing data and abnormal data hinder its development. To address missing data problems, a new method using FNN-based tensor model was proposed and evaluated through experiments, showing better performance compared to other technologies and models. The method was tested on real data from Guangzhou and Tianjin, demonstrating its effectiveness.