[1]张惠玲,孔德学,余涛,等.基于LLEKM和马尔科夫链的城市轻型车行驶工况构建[J].长安大学学报(自然科学版),2021,41(5):118-126.
　ZHANG Hui ling,KONG De xue,YU Tao,et al.Construction of urban driving cycle of lightduty vehiclebased on LLEKM and Markov chain[J].Journal of Chang’an University (Natural Science Edition),2021,41(5):118-126.
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基于LLEKM和马尔科夫链的城市轻型车行驶工况构建()
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长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
第41卷
期数:
2021年5期
页码:
118-126
栏目:
交通工程
出版日期:
2021-09-15

文章信息/Info

Title:
Construction of urban driving cycle of lightduty vehiclebased on LLEKM and Markov chain
作者:
张惠玲; 孔德学; 余涛; 敖谷昌; 邵毅明
(1. 重庆交通大学 山地城市交通系统与安全重庆市重点实验室，重庆 400074； 2. 重庆交通大学交通运输学院，重庆 400074； 3. 武汉理工大学 智能交通系统研究中心，湖北 武汉 430063)
Author(s):
ZHANG Huiling1; 2;  KONG Dexue3;  YU Tao2;  AO Guchang1; 2;  SHAO Yiming
(1. Chongqing Key Lab of Traffic System & Safety in Mountain Cities, Chongqing Jiaotong University,Chongqing 400074, China; 2. College of Traffic & Transportation, Chongqing Jiaotong University,Chongqing 400074, China; 3. Intelligent Transportation Systems Research Center,Wuhan University of Technology, Wuhan 430063, Hubei, China)
关键词:
交通工程; 行驶工况; 局部线性嵌入; K均值聚类; 马尔科夫链
Keywords:
traffic engineering;  driving cycle;  locally linear embedding;  Kmeans clustering;  Markov chain
文献标志码:
A
摘要:
为客观描述机动车在道路上的运行特征，针对传统城市道路标准行驶工况构建方法的弊端，提出一种基于局部线性嵌入与K均值聚类(LLEKM)和马尔科夫链的汽车工况构建方法，并对该方法的应用准确性进行研究。首先，结合道路交通条件对实测样本数据进行除噪平滑处理，根据运动学片段定义将其划分为1 754个运动学片段，并选取16个运动学参数构建特征参数矩阵。其次，利用局部线性嵌入算法降维约简特征参数矩阵，解决了由数据压缩重构导致的线性结构丢失的问题；通过K均值聚类分析，计算不同类型数据集中各行驶状态时间占比的差异性，得到相应类型下城市道路汽车的行驶特点。然后，以每类中各相邻行驶状态的统计量确定状态转移概率矩阵，采用马尔科夫链方法从每类中选取特征参数误差最小的运动学片段，按照时间比例合成代表工况。最后，以厦门市3个月内同一辆轻型汽车的行驶数据为例展开实证分析，验证所提方法的准确性。研究结果表明：与样本数据和传统方法构建的工况相比，所提方法构建的轻型车行驶工况主要特征参数与样本数据吻合度更好，平均相对误差从9.69%降低到4.47%；提出方法的速度加速度联合概率分布误差明显降低，行驶工况构建精度得到了提高；对比分析现行方法构建的标准工况，该方法能更加真实地反映厦门市轻型车的实际运行状况。
Abstract:
In order to objectively describe the operating characteristics of vehicles on the road, a construction method of driving cycle based on locally linear embedding, Kmeans clustering (LLEKM) and Markov chain was proposed to address the shortcomings of the traditional urban road standard driving cycle construction method. The application accuracy of the method was verified. Firstly, combining with the road traffic conditions, the measured sample data was denoised and smoothed. According to the kinematics sequence definition, it was divided into 1 754 kinematics sequences and 16 kinematics parameters were selected to construct a characteristic parameter matrix. Secondly, the locally linear embedding algorithm, which was used for dimensionality reduction and adopted to solve the problem of linear structure loss caused by data compression and reconstruction. Thirdly, through the Kmeans cluster analysis, the variability of each driving state time ratio in different types of data set was calculate, and the driving characteristics of the corresponding types of urban road vehicles were obtained. Then within each category, the state transition probability matrix was confirmed according to the statistics of the adjacent driving states, and the kinematic sequence with minimum error of characteristic parameter was selected by using Markov chain, and the typical driving cycle was synthesized according to the time scale. Finally, the driving data of the same lightduty vehicle in Xiamen city within 3 months was taken as an example, empirical analysis was carried out to verify the accuracy of the proposed method. The results show that compared with sample data and driving cycle constructed by traditional methods, the driving cycle constructed by using the proposed method has a better similarity with sample data in the aspects of main characteristic parameters, and the average relative error rate is reduced from 9.69% to 4.47%. The velocityacceleration joint probability distribution error of the proposed method is obviously reduced, and the driving cycle construction accuracy is improved. Comparing and analyzing the standard driving cycle constructed by current methods, this method can better reflect the actual operating conditions of lightduty vehicles in Xiamen. 5 tabs, 8 figs, 26 refs.

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更新日期/Last Update: 2021-09-30