Deep learning approaches to short term traffic forecasting
Capturing the spatial temporal relation in historic traffic data
T.I. Kuiper (TU Delft - Electrical Engineering, Mathematics and Computer Science)
E. Congeduti – Mentor (TU Delft - Computer Science & Engineering-Teaching Team)
George Iosifidis – Graduation committee member (TU Delft - Networked Systems)
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Abstract
The amount of cars on the roads is increasing at a rapid pace, causing traffic jams to become commonplace. One way to decrease the amount of traffic congestion is by building an Intelligent Transportation System (ITS) which helps traffic flow optimally. An important tool for an ITS is short term traffic forecasting. Better forecasts will enable the ITS to proactively prevent congestion. Recent years have seen a great increase in the availability of traffic data. As a result deep learning approaches have begun to emerge as models of choice in the short term traffic forecasting domain. Among deep learning approaches Long Short Term Memory (LSTM) and Temporal Convolutional Networks (TCN) have both shown state-of-the-art performance in general forecasting tasks as well as promising results in traffic forecasting. This work has compared both of these approaches in terms of capturing the temporal spatial correlation and scalability. The LSTM showed more ability to capture the temporal spatial correlation while both architectures seemed equally scalable.