摘要The vehicle operation condition for a certain kind of vehicle is used to describe the relationship between vehicle speed and time in a specific traffic environment. The experimental group uses the acquisition device dash cam to record the driving data of a light vehicle in Shanghai for research to quickly and accurately build the operation condition of the vehicle. The information of three different periods of time for three consecutive times is also collected with the sampling frequency of 1 Hz. We use smooth processing, elimination, attribution, and sliding window method to preprocess the huge data of vehicle operating condition for studying the driving condition of automobile. A total of 724 time discontinuities are divided into three types for different processing, smooth processing with smooth function of moving average filter to eliminate the abnormal value of speed, and idle processing with long-term parking. Thereafter, the method of sliding window is used to process the idle speed data. Then, the kinematic segments are cut, and the feature values are extracted. Finally, the dimension of 13 features is reduced to four principal components. On the basis of three methods, such as contour coefficient, K-means clustering analysis method is used to continuously verify and divide the dataset into three categories, namely, stop and go, high-speed driving, and low-speed driving, which are consistent with the reality. According to the time proportion of each category, the most representative 15 segments are selected to construct the 1254 s long steam. The error rate of the result is less than 10%. The construction model of the vehicle operation condition is very detailed for the data processing process of the vehicle operation condition, and the model constantly seeks optimization on the basis of the initial clustering center. This work provides a reference for the establishment of the vehicle performance research in line with the traffic and actual operation conditions of specific cities in China.
Abstract：The vehicle operation condition for a certain kind of vehicle is used to describe the relationship between vehicle speed and time in a specific traffic environment. The experimental group uses the acquisition device dash cam to record the driving data of a light vehicle in Shanghai for research to quickly and accurately build the operation condition of the vehicle. The information of three different periods of time for three consecutive times is also collected with the sampling frequency of 1 Hz. We use smooth processing, elimination, attribution, and sliding window method to preprocess the huge data of vehicle operating condition for studying the driving condition of automobile. A total of 724 time discontinuities are divided into three types for different processing, smooth processing with smooth function of moving average filter to eliminate the abnormal value of speed, and idle processing with long-term parking. Thereafter, the method of sliding window is used to process the idle speed data. Then, the kinematic segments are cut, and the feature values are extracted. Finally, the dimension of 13 features is reduced to four principal components. On the basis of three methods, such as contour coefficient, K-means clustering analysis method is used to continuously verify and divide the dataset into three categories, namely, stop and go, high-speed driving, and low-speed driving, which are consistent with the reality. According to the time proportion of each category, the most representative 15 segments are selected to construct the 1254 s long steam. The error rate of the result is less than 10%. The construction model of the vehicle operation condition is very detailed for the data processing process of the vehicle operation condition, and the model constantly seeks optimization on the basis of the initial clustering center. This work provides a reference for the establishment of the vehicle performance research in line with the traffic and actual operation conditions of specific cities in China.
基金资助:Supported by the National Social Science Fund Project (No.15BTJ017)
通讯作者:
WANG Wen-qi
E-mail: w1449550206@163.com
引用本文:
汪雯琦, 高广阔, 王子鉴, 梁易鑫. Construction Method of Automobile Operation Condition[J]. Journal of Highway and Transportation Research and Development, 2021, 15(1): 98-110.
WANG Wen-qi, GAO Guang-kuo, WANG Zi-jian, LIANG Yi-xin. Construction Method of Automobile Operation Condition. Journal of Highway and Transportation Research and Development, 2021, 15(1): 98-110.
[1] JIANG Ping, SHI Qin, CHEN Wu-wei, et al. A Research on the Construction of City Road Driving Cycle Based on Wavelet Analysis[J]. Automotive Engineering, 2011,33(1):70-73. (in Chinese)
[2] GALGAMUWA U, PERERA L, BANDARA S. Developing a General Methodology for Driving Cycle Construction:Comparison of Various Established Driving Cycles in the World to Propose a General Approach[J]. Journal of Transportation Technologies,2015,5(4):191-203.
[3] MINGYUE M, BENEDIKT W, FERIT K, et al. A Statistical Method for Driving Cycle Construction Based on Path Geometry[C]//Proceedings of 2013 the International Conference on Remote Sensing, Environment and Transportation Engineering. Nanjing:Atlantis Press, 2013:890-893.
[4] GAO X, ZHANG B, XIONG X, et al. Construction and Analysis of the Dalian Driving Cycle[J]. International Journal of Control & Automation,2015,8(6):363-368.
[5] DIMARATOS A, TSOKOLIS D, FONTARAS G, et al. Comparative Evaluation of the Effect of Various Technologies on Light-duty Vehicle CO2, Emissions over NEDC and WLTP[J]. Transportation Research Procedia, 2016, 14:3169-3178.
[6] PREVEDOUROS P D, MITROPOULOS L K. Life Cycle Emissions and Cost Study of Light Duty Vehicles[J]. Transportation Research Procedia, 2016,15:749-760.
[7] DEMUYNCK J, BOSTEELS D, DE PAEPE M, et al. Recommendations for the New WLTP Cycle Based on an Analysis of Vehicle Emission Measurements on NEDC and CADC[J]. Energy Policy, 2012, 49:234-242.
[8] FOTOUHI A, MONTAZERI-GH M. Tehran Driving Cycle Development Using the K-means Clustering Method[J]. Scientia Iranica, 2013,20(2):286-293.
[9] PENG Yu-hui, YANG Hui-bao, LI Meng-liang, et al. Research on the Construction Method of Driving Cycle for the City Car Based on K-means Cluster Analysis[J]. Automobile Technology, 2017(11):13-18. (in Chinese)
[10] HU Chen, WU Xiao-gang, LI Xiao-jun, et al. Construction of Harbin City Driving Cycle[J]. Journal of Harbin University of Science and Technology, 2014, 19(1):85-89. (in Chinese)
[11] HU Zhi-yuan, QIN Yan, TAN Pi-qiang, et al. Large-sample-based Car-driving Cycle in Shanghai City[J]. Journal of Tongji University:Natural Science Edition, 2015, 43(10):1523-1527. (in Chinese)
[12] SHI Qin, QIU Duo-yang, ZHOU Jie-yu. Driving Cycle Construction and Accuracy Analysis Based on Combined Clustering Technique[J]. Automotive Engineering, 2012,34(2):164-169. (in Chinese)
[13] ZHENG Dian-yu, WU Xiao-gang, CHEN Han, et al. Construction of Driving Conditions of Harbin Urban Passenger Cars[J]. Journal of Highway and Transportation Research and Development, 2017, 34(4):101-107. (in Chinese)
[14] ZHANG Guang-xin, SUN Jin-wei, ZHANG Chuan-zhen, et al. Research on Motor Vehicle Emission Factors and Control Measures[J]. Energy Conservation & Environmental Protection in Transportation, 2013(2):51-55. (in Chinese)
[15] LIN J, NIEMEIER D A. An Exploratory Analysis Comparing a Stochastic Driving Cycle to California's Regulatory Cycle[J]. Atmospheric Environment, 2002, 36(38):5759-5770.
[16] KARANDE S. OLSON M, SAHA B. Development of Representative Vehicle Drive Cycles for Hybrid Applications[EB/OL]. (2014-04-01)[2019-05-06]. https://www.sae.org/publications/technical-papers/content/2014-01-1900/.
[17] LI Ning. Construction and Research on Driving Conditions of Urban Road Vehicles[D]. Wuhan:Wuhan University of Technology, 2005. (in Chinese)
[18] LI Yang. Research on Vehicle Driving Conditions Based on Clustering Algorithm[D]. Beijing:Beijing University of Technology, 2016. (in Chinese)
[19] KNEZ M, MUNEER T, JEREB B, et al. The Estimation of a Driving Cycle for Celje and a Comparison to Other European Cities[J]. Sustainable Cities and Society, 2014, 11:56-60.
[20] HO S H, WONG Y D, CHANG V W C. Developing Singapore Driving Cycle for Passenger Cars to Estimate Fuel Consumption and Vehicular Emissions[J]. Atmospheric Environment, 2014, 97:353-362.
[21] JIANG Ping. Research on Construction of Urban Mixed Road Driving Conditions[D]. Hefei:Hefei University of Technology, 2011. (in Chinese)
[22] ZHOU Ji-guo, ZHANG Min-jiang, XUE Xiao-feng. Analysis on Critical Wind Speed for Driving Based on Fluid-solid Coupling[J]. Journal of Highway and Transportation Research and Development, 2019, 36(3):144-151. (in Chinese)
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2021, Vol. 15 
