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.
Highlights
Travel time is an important measurement for evaluating the performance of traffic management strategies.[1]
Taking into account the above reasons, and with the goal of improving the accuracy of travel time estimation for arterial, we put forward arterial travel time estimation method using Sydney Coordinated Adaptive Traffic System (SCATS) traffic data based on K-nearest neighbor (KNN)–least squares support vector regression (LSSVR) model
In order to obtain the data time series of the SCATS traffic control system with a fixed sampling interval, this article proposes the concept of a virtual time series
Summary
Travel time is an important measurement for evaluating the performance of traffic management strategies.[1]. Cheu et al.[28] introduced a model to estimate average link travel time of signalized arterials using data obtained from detectors in SCATS. Taking into account the above reasons, and with the goal of improving the accuracy of travel time estimation for arterial, we put forward arterial travel time estimation method using SCATS traffic data based on K-nearest neighbor (KNN)–least squares support vector regression (LSSVR) model. In order to obtain the data time series of the SCATS traffic control system with a fixed sampling interval, this article proposes the concept of a virtual time series. The actual sampling interval of the SCATS traffic control system is still the green signal phase, while the virtual sampling interval is the setting time length. In the virtual sampling interval j, the cycle length and the green times can be approximated as the Arterial travel time estimation based on KNN-LSSVM model.
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