Prediction of Traffic Congestion Based on LSTM through Correction of Missing Temporal and Spatial Data

With the rapid increase in vehicle use during the fourth Industrial Revolution, road resources have reached their supply limit. Active studies have therefore been conducted on intelligent transportation systems (ITSs) to realize traffic management systems utilizing fewer resources. As part of an ITS...

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Veröffentlicht in:	IEEE access 2020-01, Vol.8, p.1-1
Hauptverfasser:	Shin, Dong-Hoon, Chung, Kyungyong, Park, Roy C.
Format:	Artikel
Sprache:	eng
Schlagworte:	Big Data-based AI Data collection Data models Deep Learning Feature extraction Intelligent Transportation System Intelligent transportation systems Long Short-Term Memory Machine learning Management systems Missing data Missing Data Correction Model accuracy Outliers (statistics) Predictive models Real-time systems Resource management Roads Spatial data Traffic Traffic congestion Traffic flow Traffic information Traffic management
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