A Study of Comparison of k-NN Model and Time-Varying Coefficient Model for Predicting Travel Time on Freeways

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In recent years, the government is actively promoting Intelligent Transportation System (ITS), and Advanced Traveler Information System (ATIS) is a subsystem of ITS. Travel time prediction is a very important of ATIS. When drivers have to make a decision, it is more important for drivers to use suitable traffic information. Traffic information will allow drivers to select appropriate routes and departure time to avoid congestion and arrive in the destination with the shortest time. In this study, the probe vehicles collect real-time traffic information, and use the k-NN model and Time-Varying Coefficients (TVC) model to predict the future travel time, respectively. Evaluation and Comparison of two models for forecasting the results, hope to provide accurate forecasts of travel time to travelers departure time or route choice decision-making judgements based on. We use the 1st National Freeway Yangmei to Taishan Toll Station as the actual test object. The testing results show that k-NN model and TVC model are high precision prediction, and k-NN model predict better than TVC model. The prediction method can actually use on the highway, and can provide accurate prediction of travel time to drivers.