Modified K-Means Clustering for Travel Time Prediction Based on Historical Traffic Data

Abstract. Prediction of travel time has major concern in the research domain of Intelligent Transportation Systems (ITS). Clustering strategy can be used as a powerful tool of discovering hidden knowledge that can easily be applied on historical traffic data to predict accurate travel time. In our Modified K-means Clustering (MKC) approach, a set of historical data is portioned into a group of meaningful sub-classes (also known as clusters) based on travel time, frequency of travel time and velocity for a specific road segment and time group. With the use of same set of historical travel time estimates, comparison is also made to the forecasting results of other three methods: Successive Moving Average (SMA), Chain Average (CA) and Naïve Bayesian Classification (NBC) method. The results suggest that the travel times for the study periods could be predicted by the proposed method with the minimum Mean Absolute Relative Error (MARE)