STPNet: Quantifying the Uncertainty of Electric Vehicle Charging Demand via Long-Term Spatiotemporal Traffic Flow Prediction Intervals

Keyphrases

• Prediction Interval 100%
• Traffic Flow Prediction 100%
• Spatiotemporal 100%
• Spatio-temporal Traffic 100%
• Periodic Network 100%
• Electric Vehicle Charging Demand 100%
• Traffic Flow Data 60%
• Traffic Flow 40%
• Performance-based 20%
• Learning Techniques 20%
• Loess 20%
• Efficient Operation 20%
• Multi-location 20%
• Queuing Model 20%
• Demand-driven 20%
• Spatiotemporal Pattern 20%
• Loss Function 20%
• C(K) 20%
• Periodic Pattern 20%
• Electric Vehicle Charging Station 20%
• Encoder-decoder Architecture 20%
• Charging Demand Prediction 20%
• Spatio-temporal Series 20%
• Pattern Learning 20%
• Time Series Decomposition 20%
• Decomposition Strategy 20%
• Long Short-term Memory Units 20%
• Deep Learning Prediction 20%
• Convolutional Long Short-term Memory (ConvLSTM) 20%
• Seasonal Decomposition 20%
• Trend Decomposition 20%
• Spatio-temporal Prediction 20%
• Prediction Framework 20%

Computer Science

• Traffic Flow Prediction 100%
• Prediction Interval 100%
• Flow Information 60%
• Efficient Operation 20%
• Deep Learning Method 20%
• Prediction Framework 20%
• Long Short-Term Memory Network 20%
• Multiple Location 20%
• queueing model 20%
• Learning Technique 20%