Arterial travel time estimation method using SCATS traffic data based on KNN-LSSVR model

Abstract

In order to improve the effect of estimating travel time and provide more precise and reliable traffic information to traffic management department and travelers, we proposed an arterial travel time estimation method using Sydney Coordinated Adaptive Traffic System traffic data based on K-nearest neighbor–least squares support vector regression model. First, the virtual time series is constructed by analyzing the characteristics of the inconsistent time intervals of Sydney Coordinated Adaptive Traffic System traffic data. Second, the K-nearest neighbor method was used to search the K similarity patterns matching the current traffic pattern and obtain K travel time data. Then, the least squares support vector regression model was used to perform travel time estimation. Finally, case validation is carried out using the measured data of Sydney Coordinated Adaptive Traffic System traffic control system. The estimation results demonstrate that the travel time estimation accuracy of proposed method outperforms the other two methods.