Applying Hybrid Lstm-Gru Model Based on Heterogeneous Data Sources for Traffic Speed Prediction in Urban Areas

Applying Hybrid Lstm-Gru Model Based on Heterogeneous Data Sources for Traffic Speed Prediction in Urban Areas

With the advent of the Internet of Things (IoT), it has become possible to have a variety of data sets generated through numerous types of sensors deployed across large urban areas, thus empowering the notion of smart cities. In smart cities, various types of sensors may fall into different administ...

Full description

Bibliographic Details
Main Authors: Chughtai, J.-U.-R (Author), Haq, I.U (Author), Shafiq, O. (Author), Zafar, N. (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
Application program interfaces
Application programming interfaces (API)
Application programs
CNN
Convolutional neural network
Convolutional neural networks
Data set
Feature space
Gated recurrent unit
GRU
Heterogeneous data sources
Intelligent systems
Intelligent vehicle highway systems
Internet of things
IoT
ITS
Long short-term memory
LSTM
Mean square error
Model-based OPC
Smart city
Speed prediction
Time series analysis
Traffic control
Traffic speed
Urban areas
Online Access:View Fulltext in Publisher

Internet

View Fulltext in Publisher

Similar Items