Dynamic Toll Prediction Using Historical Data on Toll Roads: Case Study of the I-66 Inner Beltway
This work addresses travel decision-making for users of dynamic tolling systems, but it is incremental as it applies existing machine learning methods to a specific case study.
The study tackled the problem of predicting dynamic toll prices and travel time differences on the I-66 Inner Beltway, finding that random forest models significantly outperformed a baseline model, reducing mean absolute error from $2.5-$6 to $1.5-$2.5 for toll price predictions over a 30-minute horizon.
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.