文章基本信息

标题：Parameterized Derivative-free Optimization Approach for Car-following Model Calibration
本地全文：下载
作者：Xingyu Zhou ; Zejiang Wang ; Adrian Cosio 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：20
页码：876-881
DOI：10.1016/j.ifacol.2021.11.282
语种：English
出版社：Elsevier
摘要：AbstractCalibration of car-following models based on empirical trajectory data is a fundamental problem in traffic simulations. As a common practice, optimization problems are formulated to calibrate the model parameters under the objective of minimizing the “difference” between observed vehicle movements and their simulated correspondences. Oftentimes, high degrees of nonlinearity, nonconvexity, and non-smoothness are embedded inside the cost functions. In consequence, derivative-based optimization methods are found to be impractical due to the laboriousness of gradients computations. To overcome this obstacle, derivative-free approaches are widely adopted for seeking the optimal model parameters. In this paper, we explore a new derivative-free cost-minimization methodology called Parameterized Derivative-free Optimization (PDFO) for tackling the car-following model calibration problem. The accuracy and efficiency of the PDFO are examined with the NGSIM data sets. Besides, comparative studies are conducted to investigate the performance differences between the PDFO and other frequently utilized derivative-free optimization techniques including the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA).
关键词：KeywordsCar-following modelderivative-free optimizationintelligent driver model (IDM)