Comparison of Geometric-based Travel Time Prediction between Cars and Trucks

Abstract

Travel time can be estimated using various approaches, such as historical data, traffic data, machine learning, and road geometry. In this paper, our focus is on prediction based on road geometry. By utilizing the curvature data of a road computed from GPS data, it is possible to determine the design speed of a specific road section. This information can then be used to estimate the time it would take to travel that particular stretch of road. For this paper, we test the algorithm on a 1.7km road in Kulim, Kedah, and observe its reliability of prediction for cars and trucks. While it provides accurate predictions for cars, it produces higher errors for trucks, implying that the approach is more suitable for cars than heavier vehicles. Insights gained from this research can inform future efforts to refine prediction models and enhance the accuracy and reliability of travel time estimation based on road geometry, especially for heavy vehicles.