The Creation of a Representative Driving Cycle based on Intelligent Transportation System (ITS) and a Mathematically Statistical Algorithm: a Case Study of Changchun (China)

This paper is concerned with the development of a representative driving cycle that has advantages in accuracy and robustness. There are two aspects to achieve the representativeness of the developed driving cycle. Foremost, the on-road driving patterns derive from a sufficient and objective database. Secondly, the simplicity and accuracy of the construction methodology are taken into full deliberation. To achieve these, the first issue is solved by making a combination of the official statistical data and Intelligent Transportation System, instead of determining the test routes via subjectivity and experience. Specifically, the official statistical data support the road classification and characteristic. In parallel, it is instrumental to take advantage of traffic information network provided by Google Waze intelligent application. The second issue is accomplished with dynamic clustering algorithm, which is tremendously appealing in practice. In the proposed method, the comprehensive principal component score (CPCS) is created to cluster the micro-trips into more homogeneous groups of observations. The Euclidean distance and iterative rapidity of convergence illustrate that CPCS-based dynamic clustering outperforms poly-principal components-based with respect to clustering performance and complexity. The robustness assessments verify the developed driving cycle matches the real-world driving cycle characteristics with high resolution under well-designed experiments and simulations.

• Record URL:
  • https://doi.org/10.1016/j.scs.2018.05.031
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S221067071731613X
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/22106707
• Supplemental Notes:
  • © 2018 Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Zhao, Jingyuan
  • Gao, Yinhan
  • Guo, Jianhua
  • Chu, Liang
• Publication Date: 2018-10

Language

• English

Media Info

• Media Type: Web
• Features: Figures; Maps; Photos; References; Tables;
• Pagination: pp 301-313
• Serial:
  • Sustainable Cities and Society
  • Volume: 42
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2210-6707
  • Serial URL: http://www.sciencedirect.com/science/journal/22106707?sdc=2

Subject/Index Terms

• TRT Terms: Algorithms; Case studies; Intelligent transportation systems; Statistical analysis; Traffic characteristics; Traffic flow
• Geographic Terms: Changchun (China)
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01680732
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 17 2018 5:19PM