On the Impact of Floating Car Data and Data Fusion on the Prediction of the Traffic Density, Flow and Speed Using an Error Recurrent Convolutional Neural Network

On the Impact of Floating Car Data and Data Fusion on the Prediction of the Traffic Density, Flow and Speed Using an Error Recurrent Convolutional Neural Network

Traffic prediction helps mitigate the impact of traffic congestion. The accuracy of traffic predictions depends on the availability of the data used for the prediction as well as the prediction model. Data from fixed traffic detectors is only available at certain locations. On the other hand, connec...

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Bibliographic Details
Main Authors: Jesus Mena-Oreja, Javier Gozalvez
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Traffic prediction
floating car data
connected vehicle
induction loops
data fusion
neural networks
Online Access:https://ieeexplore.ieee.org/document/9548099/

Internet

https://ieeexplore.ieee.org/document/9548099/

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