| 引用本文: | 陈 宝,谢光毅,黄 春,付江华.基于改进 K 均值和马尔科夫链的公交车行驶工况构建(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2024，41（3）：18-25 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 摘要: |
| 目的 为填补目前缺少符合重庆城区地形特点的公交车行驶工况空白。 方法 提出一种改进的 K 均值聚类算
法与马尔科夫链结合的行驶工况构建方法。 通过滤波算法对采集到的重庆城区 805 路公交车有效行驶数据进行
平滑处理,从平滑后的行驶数据中提取出 1 721 条运动学片段;采用核主成分分析法对各运动学片段的特征参数矩
阵进行降维处理,以粒子群算法选择适合的 K 均值初始聚类中心,并通过改进后的 K 均值聚类算法对运动学片段
进行分类标记;最后通过马尔科夫链筛选出合理的运动学片段,构建出时长为 1 310 s 的重庆城区公交车行驶工
况。 结果 将构建的行驶工况与实车采集数据及国内外部分典型工况进行对比,结果表明:所构建的工况与原始数
据在速度-加速度的联合分布趋势一致,构建的行驶工况与实车采集数据各特征值误差均小于 7%,所构建的工况
与原始数据特征高度吻合且能较好地反映出重庆城区公交车怠速时间比例高、加减速频繁等符合区域特点的实际
交通状况。 结论 构建的工况能为重庆市公交车行驶路线规划、排放及油耗测试等方面提供基础标准,同时其涉及
的研究方法能为新能源汽车能量管理、控制策略及其他城市工况构建和城市公交线路优化提供参考。 |
| 关键词: 公交车 行驶工况 核主成分分析 K 均值聚类算法 马尔科夫链 |
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| Construction of Bus Driving Cycle Based on Improved K-means and Markov Chains |
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CHEN Bao，XIE Guangyi，HUANG Chun，FU Jianghua
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School of Vehicle Engineering Chongqing University of Technology Chongqing 400054 China
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| Abstract: |
| Objective The research in this paper aims to fill the gap of the current lack of bus driving cycles in line with
the characteristics of the urban terrain in Chongqing. Methods An improved K-mean clustering algorithm combined with
a Markov chain was proposed to construct driving cycles. The effective driving data of bus No. 805 in urban areas of
Chongqing were smoothed using a filtering algorithm from which 1 721 kinetic segments were extracted. The feature
parameter matrices of each kinetic segment were dimensionally reduced using kernel principal component analysis
suitable initial cluster centers were selected with the particle swarm algorithm for K-means and the kinetic segments were
classified and labeled using the improved K-means clustering algorithm. Finally reasonable kinetic segments were
selected through the Markov chain to construct a bus driving cycle in urban areas of Chongqing with a duration of 1 310
seconds. Results The constructed driving cycle was compared with the data collected from real vehicles and some typical
cycles at home and abroad. The results showed that the constructed cycle exhibited consistent trends in the joint
distribution of speed and acceleration with the original data and the errors of the characteristic values of the constructed
driving cycle compared with the data collected from real vehicles were all less than 7%. The constructed cycle highly
matched the characteristics of the original data and can well reflect the high idle time ratio frequent acceleration and
deceleration and other actual traffic conditions in urban areas of Chongqing. Conclusion The constructed cycle can provide a basic standard for bus route planning emission testing and fuel consumption testing in Chongqing and the research methods involved can serve as a reference for energy management of new energy vehicles control strategies as
well as city driving cycle construction and optimization of city bus routes. |
| Key words: bus driving cycle kernel principal component analysis K-means clustering algorithm Markov chain |
