A Car-following Model Coupling Machine Learning and Dynamic

Abstract

Abstract:

So far, the car- following model is mostly built by dynamic and machine learning algorithms, there remains no researches on the building of car- following model by coupling the two methods. On the basis of the linear combination forecast, this paper improves the objective function of the optimal weighting method and coupled the Gipps model and BP model based on back propagation neural network to establish the linear combination car- following model (LC- CF). The results show that the speed forecasted by BP model peforms better in accuracy and the speed forecasted by the Gipps model peforms better in security. LCCF model can achieve the purpose of controlling the accuracy and security of the speed forecasting by adjusting the parameters and controlling the weight of BP model and Gipps model in the LC-CF model.

Key words: intelligent transportation, road transportation, linear combination forecasting, dynamic model, BP-neural network, car-following

摘要：

目前，跟驰模型的建立主要基于动力学方法和机器学习算法，将两者耦合起来建立跟驰模型的研究还没有.以线性组合预测为基础，对最优加权法中的目标函数进行改进，将经典的Gipps 模型和基于BP 神经网络的跟驰模型(BP Car-following Model, BP)耦合起来，建立线性组合车辆跟驰模型(Linear Combination Car-following Model, LC-CF).结果表明：BP 模型的预测结果更加贴近真实值，Gipps 模型的预测结果更加贴近安全值； LC-CF模型可以通过调整参数，来控制BP模型和Gipps 模型在LC-CF模型中的权重，进而达到控制预测速度的真实性和安全性的目的.

关键词: 智能交通, 道路运输, 线性组合预测, 动力学模型, BP神经网络, 车辆跟驰

CLC Number:

U491.2

DING Dian-dian, SUN Lei, CHEN Song. A Car-following Model Coupling Machine Learning and Dynamic[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(6): 33-39.

丁点点，孙磊，陈松. 机器学习—动力学耦合车辆跟驰模型[J]. 交通运输系统工程与信息, 2017, 17(6): 33-39.

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URL: http://www.tseit.org.cn/EN/abstract/abstract19517.shtml

http://www.tseit.org.cn/EN/Y2017/V17/I6/33

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