| dc.description.abstract | In recent years there has been a vast increase in available data with the ad-
vancement of smart cities. In the domain of Intelligent Transportation Systems
(ITS) this modernisation can positively effect transportation networks, thus cut-
ting down travel time, increase efficacy, and reduce environmental impact from
vehicles.
Norwegian Public Roads Administration (NPRA) is currently deploying a new
vehicle detector system named Datainn on all public roads in Norway. Datainn
sends metadata on all detected vehicles in real time. This includes information
about speed, gap between vehicles, weight, and classification of vehicle type.
Many machine learning approaches has been researched in literature on how
to forecast traffic flow information. One such approach is that of using Artificial
Neural Networks (ANNs). In this research ANN based methods have been explored.
This was done by first performing a state-of-the-art Structured Literature Review
(SLR) on ANN methods in literature.
From the review, Stacked Sparse Autoencoder (SSAE) model was compared
with recent advances of Long Short-Term Memory (LSTM) and Deep Neural
Network (DNN) on four different prediction horizons. The data foundation was
the new Datainn system using traffic data from a highway around Norway s
capitol, Oslo. Further, the model performance was assessed with extended feature
vectors including more metadata from Datainn.
The results found that the LSTM model always outperformed DNN and SSAE,
although in general the performance characteristics was somewhat similar. Ex-
tending the feature vector with more variables had a negative effect on DNN,
while resulting in better performance for Recurrent Neural Network (RNN) on
long-term (60 minute) forecasting horizons. For SSAE it had a slight positive
effect, but not enough get better results than RNN or DNN. | |
