交通运输系统工程与信息

交通运输系统工程与信息 ›› 2015, Vol. 15 ›› Issue (5): 239-245.

• 案例分析 • 上一篇    下一篇

基于ARMA 预测模型的交叉口车辆碰撞风险评估

张良力*1，祝贺1，吴超仲2,3，郑安文1   

  1. 1. 武汉科技大学信息科学与工程学院，武汉430081；2. 武汉理工大学智能交通系统研究中心，武汉430063； 3. 水路公路交通安全控制与装备教育部工程研究中心，武汉430063
  • 收稿日期:2015-04-03 修回日期:2015-05-22 出版日期:2015-10-25 发布日期:2015-10-28
  • 作者简介:张良力(1981-)，男，湖北武汉人，副教授，博士.
  • 基金资助:

    国家自然科学基金(51308426，51105286 )；湖北省教育厅科学研究计划项目(B2013234).

Risk Estimation to Vehicles Collision at Intersection Based on ARMA Prediction Model

ZHANG Liang-li1, ZHU He1, WU Chao-zhong2，3, ZHENG An-wen1   

  1. 1. School of Information Science and Engineering,Wuhan University of Science and Technology,Wuhan 430081, China; 2. Intelligent Transport Systems Research Center,Wuhan University of Technology,Wuhan 430063, China; 3. Engineering Research Center for Transportation Safety, Ministry of Education,Wuhan 430063, China
  • Received:2015-04-03 Revised:2015-05-22 Online:2015-10-25 Published:2015-10-28

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被引次数

2

摘要：

车辆进入交叉口前的速度时间序列可用于预测车辆进入交叉口后若干步数速度值，利用车速预测值推算冲突方向车辆在交叉口内的行驶位移及其车间距离，可评估车辆发生碰撞的风险.针对交叉口附近车速分布符合随机序列特征，采用自回归滑动平均 (ARMA)理论进行车速时序预测建模，步骤包括时序数据相关性检查、模型p-q 定阶、解析式系数估计、适用性检验.试验结果表明：利用实测车速中的前40 个时序数据建立ARMA 模型，预测出的20 个车速值与实测值贴近，冲突方向两车车速归一化平均绝对误差分别为0.006 56 和0.003 4；利用全部60 个实测数据建立预测模型，检测预测值残差自相关函数发现其绝对值均小于0.258 2，表明所建车速预测方法适用.

关键词: 智能交通, 碰撞风险评估, 自回归滑动平均建模, 交叉路口, 车速预测

Abstract:

Speed time series collected as vehicles approaching an intersection can be used to predict several speed values as they subsequently entering it. Then, traveling tracks and spacing distances of the conflict vehicles are calculated by the predicted speed values, and the collision risk of them can be estimated. Because the speed distribution of a vehicle approaching to an intersection closes to the characteristics of random sequences, auto-regressive moving average (ARMA) theory is introduced to model the vehicle speed prediction. The modeling process includes time series data correlation test, p-q orders determination, formula coefficient estimation and model adaptability test. Test result shows that the ARMA model built by the previous 40 data of the observed speed time series could predict 20 values which are closed to the 20 observed ones. The other evidences of that are the normalized mean absolute errors of the conflict vehicles, which respectively equaled to 0.006 56 and 0.003 4. Further, the model built by all the 60 data of the observed time series is necessarily more applicable to predict vehicle speed, just as all the result values of the residual auto-correlation function test are less than 0.258 2.

Key words: intelligent transportation, collision risk estimation, auto-regressive moving average (ARMA), intersection, vehicle speed prediction

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