基于主成分分析与BP 神经元网络的驾驶能耗组合预测模型研究

近年来交通领域能源消耗问题备受关注，本文从微观交通能耗预测出发，以实现北京市快速路基础路段的油耗预测为目的，基于出租车车载OBD/GPS终端，提取驾驶员微观驾驶行为数据，建立基于主成分分析与BP神经元网络的油耗组合预测模型，实现北京市快速路基础路段油耗的准确预测.结果表明：速度均值及标准差、最大车速、工况百分比、加速度及减速度均值、行驶距离和动能对油耗影响程度相对较高；同时模型能够实现城市快速路基础路段能耗的有效预测，预测精度达到92.46%.该方法的研究为城市交通能源消耗的监管与把控提供了支持.

Prediction Model of Driving Energy Consumption Based on PCA and BP Network

Nowadays, society pays much attention to the problems of fuel consumption. This paper concerns about prediction of microcosmic energy consumption, and its purpose is to realize fuel consumptions of Beijing basic freeway section. Based on OBD/GPS terminal installation on taxis, we extract driving behavior’s data of taxi drivers, select main relevant indexes, set up the prediction model of fuel consumption, and realize accurate prediction of fuel consumption in Beijing basic freeway section. Results show that average speed, standard deviation of speed, max speed, rate of operating condition, average acceleration and deceleration, distance and energy have greater influence on fuel consumption; PCA and neural network combination model can realize energy consumption prediction effectively, and the accuracy of prediction can reach 92.46%. This research can provide strong supports on monitor and regulation of traffic energy consumption.