A Hybrid Traffic Forecasting Model for Urban Environments Based on Convolutional and Recurrent Neural Networks-Reference-Cited by-同舟云学术

A Hybrid Traffic Forecasting Model for Urban Environments Based on Convolutional and Recurrent Neural Networks

Published:2023 Issue: Volume:68 Page:441-446
ISSN:2352-1465
Container-title:Transportation Research Procedia
language:en
Short-container-title:Transportation Research Procedia

Author:

Shepelev Vladimir,Slobodin Ivan,Almetova Zlata,Nevolin Dmitry,Shvecov Andrei

Funder

Russian Science Foundation

Russian Foundation for Basic Research

Publisher

Elsevier BV

Subject

General Medicine

Reference38 articles.

1. Mapping real-time air pollution health risk for environmental management: Combining mobile and stationary air pollution monitoring with neural network models;Adams;Journal of Environmental Management,2016

2. Long short-term memory (LSTM) recurrent neural network (RNN) based traffic forecasting for intelligent transportation;Baskar;AIP Conference Proceedings,2022

3. Areas of focus in ensuring the environmental safety of motor transport;Boryaev;Transportation Research Procedia,2020

4. Traffic Load Estimation from Structural Health Monitoring sensors using supervised learning;Burrello;Sustainable Computing: Informatics and Systems,2022

5. Deciphering urban traffic impacts on air quality by deep learning and emission inventory;Du;Journal of Environmental Sciences (China),2023

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. TC-GCN: Triple cross-attention and graph convolutional network for traffic forecasting;Information Fusion;2024-05

2. Attentive graph structure learning embedded in deep spatial-temporal graph neural network for traffic forecasting;Applied Intelligence;2024-02

3. New deep recurrent hybrid artificial neural network for forecasting seasonal time series;Granular Computing;2024-01-10

4. Identification of Suspicious Patterns and Prevention of Telephone Fraud Incidents Using a Neural Network;2023 IEEE 24th International Conference of Young Professionals in Electron Devices and Materials (EDM);2023-06-29