Method for using activity recognition to improve ensemble forecasting for traffic systems, A
Accurate traffic forecasting is of great interest for commercial, security, and energy efficiency applications. In this work we focus on forecasting the movement of people in a building and vehicles on a roadway. Traditional forecasting methods use statistical models, learned from historical data. However, these methods fail during the presence of anomalies. In such cases, the forecast can deviate significantly from historical averages. We have developed a method to recognize the occurrence of an anomaly, and adjust the forecast to take this into account. Our approach is to first train a background ...
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