Cluster Computing

, Volume 22, Supplement 3, pp 6871–6880 | Cite as

Development of a representative driving cycle for urban buses based on the K-means cluster method

  • Peng YuhuiEmail author
  • Zhuang Yuan
  • Yang Huibao


To establish a representative driving cycle for public urban buses in Fuzhou city, real-world driving data of 18 buses’ routes was gathered. Based on acquisition of the real operational data, about 2.2 million seconds of valid data was divided into 8262 micro trips and an evaluation metric comprised of 15 characteristic parameters was established. Next, principal component analysis was applied for dimensionality reduction of the characteristic parameters and all micro trips were classified into different clusters by K-means clustering. Subsequently, the Silhouette equation was introduced as part of the cluster selection procedure. Finally, the target driving cycle, containing a 1227 second speed-time series, was developed according to a comprehensive estimation of six characteristic parameters and a maximum of the speed-acceleration frequency distribution. The experimental results verified the effectiveness and accuracy of the proposed method.


Driving cycle Micro trip Principal component analysis K-means clustering Silhouette equation 



The author would like to thank the generous support of the research project of China Automotive Test Cycles and the research program of Collaborative Innovation Center for R&D of Coach and Special Vehicle.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mechanical Engineering and AutomationFuzhou UniversityFuzhouChina
  2. 2.Collaborative Innovation Center for R&D of Coach and Special VehicleXiamen University of TechnologyXiamenChina

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