Estimating Vehicle Turn-In Rate of Expressway Rest Areas via ETC Gantry Data – An ADPC-GMM Approach

Abstract

Vehicle turn-in rate is a critical and widely adopted input for expressway rest area design and operation. With the implementation of expressway ETC gantries, the ERA turn-in rate can be further estimated by measuring the travel speed distribution via ETC gantry data. This paper proposed an adaptive density peak clustering Gaussian mixture model (ADPC-GMM) for ERA turn-in rate estimation. The ADPC algorithm is applied to generate the GMM’s inputs accommodating to the traffic characteristic of ERA expressway segments and GMM would further provide the turn-in rate estimation results. To validate the model precision, the turn-in rate data of four selected ERAs in Sichuan, China, as well as the ETC gantry data of their corresponding expressway sections are obtained. According to the estimation results, the MAE and RMSE are 0.0228 and 0.0267 for the passenger car scenario and 0.0264 and 0.0356 for the commercial truck scenario, respectively. These results are also at the lowest level compared with the results acquired from ordinary GMM, K-Means and DBSCAN algorithms. The proposed method has good applicability for vehicle turn-in rate estimation and can be deployed at different ERAs, especially those ERAs without traffic monitoring.

Keywords

• expressway rest area
• turn-in rate
• etc gantry
• adaptive density peak clustering
• gaussian mixture model