Having access to accurate travel time is of great importance for both highway network users and traffic engineers. The travel time which is currently reported on several highways is estimated by employing naïve methods and using limited sources of data. This results in unreliable and inaccurate travel time prediction and could impose delay on travelers. Therefore, the main objective of this study is short-term prediction of travel time for highways using multiple data sources including loop detectors, probe vehicles, weather condition, network, accidents, road works, and special events in order to consider the effect of different factors on travel time. To this end, two machine learning methods, K-Nearest Neighbors and Random Forest, are employed. After applying data cleaning process on datasets and combining them, the models are trained to predict and compare short-term harmonic average speed as a representative of travel time for 5-minute prediction horizons in one hour ahead. The travel time is calculated as the ratio of the length of each link and the harmonic average speed for all reporting vehicles. Hence, a model is trained for each technique to predict travel time 5 minutes ahead, 10 minutes ahead, and all the way down to 60 minutes ahead. The results confirm satisfying performance of both models in short-term travel time prediction with slightly outperformance of Random Forest model. A feature importance and sensitivity analysis also applied for the Random Forest model, and traffic variables are found as the most effective variables in predicting the travel time.
A Freeway Travel Time Prediction Method Based on An XGBoost Model
