Synthesis of real-world driving cycles using stochastic process and statistical methodology
by Tae-Kyung Lee; Zoran S. Filipi
International Journal of Vehicle Design (IJVD), Vol. 57, No. 1, 2011

Abstract: This paper proposes a procedure for synthesising real-world driving cycles to reproduce naturalistic driving patterns for arbitrary driving distances. The procedure combines stochastic processes and statistical methodologies. Vehicle dynamics equations are investigated and two states, velocity and acceleration, are selected to represent real-world driving behaviour with the Markov chain. Then, the information is extracted from the naturalistic driving data measured in Southeast Michigan in a form of transition probability matrices (TPMs). Statistical methods are utilised to guarantee the representativeness of synthesised cycles. Results demonstrate the ability to capture features of a whole category of naturalistic data with a single synthetic cycle.

Online publication date: Wed, 15-Apr-2015

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Vehicle Design (IJVD):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com