A feature extraction and deep learning approach for network traffic volume prediction considering detector reliability

Keyphrases

• Feature Extraction 100%
• Network Traffic 100%
• Deep Learning Methods 100%
• Detector Failure 100%
• Traffic Volume Forecasting 100%
• Detector Reliability 100%
• Traffic Pattern 66%
• Traffic Volume 66%
• Network Level 66%
• Japan 33%
• Congestion 33%
• Computational Results 33%
• Multilayer Perceptron 33%
• Predictive Models 33%
• Typical Days 33%
• Profile Model 33%
• Information Centres 33%
• Mean Absolute Error 33%
• Long Short-term Memory 33%
• Tokyo Olympics 33%
• COVID-19 Lockdown 33%
• Urban Traffic Control 33%
• Road Traffic Information 33%
• Lockdown Period 33%
• Three-City 33%

Computer Science

• Network Traffic 100%
• failure detector 100%
• Deep Learning 100%
• Learning Approach 100%
• Feature Extraction 100%
• Traffic Pattern 66%
• Traffic Information 33%
• Data Center 33%
• Long Short-Term Memory Network 33%
• Mean Absolute Error 33%
• Predictive Model 33%
• Traffic Control 33%
• Multilayer Perceptron 33%
• Information Centre 33%
• Reference Information 33%

Engineering

• Learning Approach 100%
• Feature Extraction 100%
• Traffic Volume 100%
• Deep Learning 100%
• Traffic Pattern 40%
• Network Level 40%
• Road 20%
• Reference Information 20%
• Japan 20%
• Traffic Control 20%
• Long Short-Term Memory 20%
• Perceptron 20%
• Mean Absolute Error 20%
• Urban Traffic 20%