Vehicle driving cycle construction method of urban roads

GUO Jia-chen; JIANG Heng; LEI Shi-ying; ZHONG Zhi-rong; ZUO Hong-fu; XU Juan

doi:10.19818/j.cnki.1671-1637.2020.06.017

Volume 20 Issue 6

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GUO Jia-chen, JIANG Heng, LEI Shi-ying, ZHONG Zhi-rong, ZUO Hong-fu, XU Juan. Vehicle driving cycle construction method of urban roads[J]. Journal of Traffic and Transportation Engineering, 2020, 20(6): 197-209. doi: 10.19818/j.cnki.1671-1637.2020.06.017

Citation:

GUO Jia-chen, JIANG Heng, LEI Shi-ying, ZHONG Zhi-rong, ZUO Hong-fu, XU Juan. Vehicle driving cycle construction method of urban roads[J]. Journal of Traffic and Transportation Engineering, 2020, 20(6): 197-209. doi: 10.19818/j.cnki.1671-1637.2020.06.017

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Vehicle driving cycle construction method of urban roads

doi: 10.19818/j.cnki.1671-1637.2020.06.017

College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, Jiangsu, China

Funds:

National Natural Science Foundation of China U1733201

National Natural Science Foundation of China U1933202

Postgraduate Research and Practice Innovation Program of Jiangsu Province KYCX20-0215

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Author Bio:
GUO Jia-chen(1995-), male, doctoralstudent, gjc@nuaa.edu.cn

ZUO Hong-fu(1959-), male, professor, PhD, rms@nuaa.edu.cn
Received Date: 2020-06-30
Publish Date: 2020-06-25

Abstract

Abstract

In order to optimize vehicle driving performance, test cycles reflecting actual road driving cycles in China were constructed. Taking the measured road data of light-duty vehicles as the data source, the vehicle driving cycle construction method of urban road was proposed. Data collection covered the main period and roads, the abnormal data were eliminated, and the multi-scale wavelet transform was introduced to reduce the noise of vehicle speed. Three-layer wavelet decomposition was used to filter the impact of ground disturbance, and the key information of vehicle speed was retained. The car kinematics segment feature system based on 9 representative characteristic parameters closely related to the driving characteristics was established. Principal component analysis and auto-encoder were used to reduce the dimension of features. K-means++clustering algorithm was used to determine the kinematics segments and the Silhouette function was introduced to filter the clustering results to replace manual selection, and determine the number of clusters of 2 categories. The distance from the corresponding cluster center was used as an indicator, 200 kinematic segments in each category that can best reflect the characteristics of the category were selected as the candidate kinematic segments. Finally, the representative kinematic segments were determined based on the minimum performance value evaluation method, the construction of vehicle driving construction was finished, and the corresponding vehicle driving cycles curves based on principal component analysis and auto-encoder were obtained respectively. Calculation results show that vehicle driving cycle construction based on principal component analysis and auto-encoder is highly representative and reasonable according to the data source. The absolute valves of relative errors between the data based on principal component analysis and the data source are mostly less than 10%. The relative errors of average speed, average driving speed, idle time ratio, acceleration time ratio, deceleration time ratio, average acceleration, acceleration standard difference, and average deceleration are 0.75%, 5.50%, 9.14%, 9.80%, 9.98%, 8.45%, 6.17% and 7.73%, respectively. Only the relative error of velocity standard deviation reaches 24.31%. Therefore, the principal component analysis has stronger comprehensive advantages than the results obtained by the auto-encoder method, and it is more suitable for vehicle driving condition construction.
- automobile engineering,
- driving cycle,
- principal component analysis,
- auto-encoder,
- kinematic segment,
- construction method

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References(38)

References

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Vehicle driving cycle construction method of urban roads

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