交通运输系统工程与信息 ›› 2016, Vol. 16 ›› Issue (5): 185-191.

• 系统工程理论与方法 • 上一篇    下一篇

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

赵晓华*,姚莹,伍毅平,陈晨,荣建   

  1. 北京工业大学北京市交通工程重点实验室,北京100124
  • 收稿日期:2016-04-25 修回日期:2016-05-12 出版日期:2016-10-25 发布日期:2016-10-25
  • 作者简介:赵晓华(1971-),女,山西太谷人,教授.
  • 基金资助:

    北京市朝阳区协同创新项目/Collaborative Innovation Project from Chaoyang District of Beijing(XC1406);国家自然科学基金/National Natural Science Foundation of China(61672067).

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

ZHAO Xiao-hua, YAO Ying, WU Yi-ping, CHEN Chen, RONG Jian   

  1. Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China
  • Received:2016-04-25 Revised:2016-05-12 Online:2016-10-25 Published:2016-10-25

摘要:

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.

关键词: 城市交通, 能耗排放, 预测模型, 驾驶行为, 神经元网络, 主成分分析

Abstract:

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.

Key words: urban traffic, energy consumption, prediction model, driving behavior, neural network, PCA

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