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Providing the users of a dynamic tolling system with predictions of tollingprices and the travel time difference between the toll road and the alternativeroutes enables them to make their travel decisions before starting their trip.This study aims to provide accurate predictions of tolling price throughtraining and testing random forest, multilayer perceptron, and long short-termmemory models and compare them with the current situation that the bestprediction is extending the current toll to the next timesteps. The predictiontime horizon includes five 6-minute time intervals ahead of the present time.The prediction performance of models over the testing set reveals that whileall the models were significantly better than the base model, the random forestoutperforms all models. For instance, while in the trained models, the meanabsolute error range is from $1.5 to $2.5 for the next six minutes to the next30 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 andits alternative route with the shortest travel time revealed that themultilayer perceptron performs marginally better than the base model. However,due to a relatively stable travel time difference, the current travel timedifference is an acceptable prediction for the next 30 minutes predictionhorizon.

Dynamic Toll Prediction Using Historical Data on Toll Roads: Case Study of the I-66 Inner Beltway