A Learning-Based Multimodel Integrated Framework for Dynamic Traffic Flow Forecasting

Keyphrases

• Traffic Flow 100%
• Learning-based 100%
• Multi-model 100%
• Dynamic Traffic Flow 100%
• Traffic Flow Forecasting 100%
• Forecasting Model 66%
• Stacked Autoencoder 66%
• Prediction Accuracy 33%
• Traffic Condition 33%
• Training Data 33%
• Single Model 33%
• Multiple Models 33%
• Labeled Data 33%
• Deep Learning Methods 33%
• Traffic Flow Data 33%
• Model Integration 33%
• Optimal Subset 33%
• Deep Learning Architectures 33%
• Integration Framework 33%
• Handcrafted Features 33%
• Feature Learning 33%
• Dependence Relation 33%
• Robust Forecasting 33%
• Implicit Relationship 33%
• Intelligent Transportation Systems 33%
• State-of-the-art Models 33%
• Traffic Flow Conditions 33%
• Efficient Deep Learning 33%

Computer Science

• Deep Learning Method 100%
• Dynamic Traffic 100%
• Autoencoder 100%
• Traffic Condition 50%
• Candidate Model 50%
• Dependence Relation 50%
• Integration Framework 50%
• Representation Learning 50%
• forecasting accuracy 50%
• Training Data 50%
• Flow Condition 50%
• Intelligent Transportation System 50%

Engineering

• Autoencoder 100%
• Deep Learning Method 100%
• Data Flow 50%
• Input Data 50%
• Flow Condition 50%
• Inherent Randomness 50%
• Limitations 50%
• Intelligent Transportation System 50%