Parametric calibration of speed–density relationships in mesoscopic traffic simulator with data mining

Abstract

Speed–density relationships are used by mesoscopic traffic simulators to represent traffic dynamics. While classical speed–density relationships provide useful insights into the traffic dynamics problem, they may be restrictive for such applications. This paper addresses the problem of calibrating speed–density relationship parameters using data mining techniques, and proposes a novel hierarchical clustering algorithm based on K-means clustering. By combining K-means with agglomerative hierarchical clustering, the proposed new algorithm is able to reduce early-stage errors inherent in agglomerative hierarchical clustering resulted in improved clustering performance. Moreover, in order to improve the precision of parametric calibration, densities and flows are utilized as variables. The proposed approach is tested against sensor data captured from the 3rd Ring Road of Beijing. The testing results show that the performance of our algorithm is better than existing solutions.