  Providing the users of a dynamic tolling system with predictions of tolling
prices and the travel time difference between the toll road and the alternative
routes enables them to make their travel decisions before starting their trip.
This study aims to provide accurate predictions of tolling price through
training and testing random forest, multilayer perceptron, and long short-term
memory models and compare them with the current situation that the best
prediction is extending the current toll to the next timesteps. The prediction
time horizon includes five 6-minute time intervals ahead of the present time.
The prediction performance of models over the testing set reveals that while
all the models were significantly better than the base model, the random forest
outperforms all models. For instance, while in the trained models, the mean
absolute error range is from $1.5 to $2.5 for the next six minutes to the next
30 minutes, respectively, the same measure in the base model is in the range of
$2.5 to $6. The prediction of travel time difference along the toll road and
its alternative route with the shortest travel time revealed that the
multilayer perceptron performs marginally better than the base model. However,
due to a relatively stable travel time difference, the current travel time
difference is an acceptable prediction for the next 30 minutes prediction
horizon.
