A robust, data-driven methodology for real-world driving cycle development
This paper develops a robust, data-driven Markov Chain method to capture real-world behaviour in a driving cycle without deconstructing the raw velocity-time sequence. The accuracy of the driving cycles developed using this method was assessed on nine metrics as a function of the number of velocity states, driving cycle length and number of Markov repetitions. The road grade was introduced using vehicle specific power and a velocity penalty. The method was demonstrated on a corpus of 1180 km from a trial of electric scooters. The accuracies of the candidate driving cycles depended most strongly on the number of Markov repetitions. The best driving cycle used 135 velocity modes, was 500 s and captured the corpus behaviour to within 5% after 1,000,000 Markov repetitions. In general, the best driving cycle reproduced the corpus behaviour better when road grade was included.
- Record URL:
-
Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/13619209
-
Supplemental Notes:
- Abstract reprinted with permission from Elsevier.
-
Authors:
- Bishop, Justin D K
- Axon, Colin J
- McCulloch, Malcolm D
- Publication Date: 2012-7
Language
- English
Media Info
- Media Type: Print
- Features: Figures; References; Tables;
- Pagination: pp 389-397
-
Serial:
- Transportation Research Part D: Transport and Environment
- Volume: 17
- Issue Number: 5
- Publisher: Elsevier
- ISSN: 1361-9209
- Serial URL: http://www.sciencedirect.com/science/journal/13619209
Subject/Index Terms
- TRT Terms: Markov chains; Running speed; Scooters
- Uncontrolled Terms: Driving cycles; Real world data; Vehicle specific power
- Subject Areas: Highways; Planning and Forecasting; Vehicles and Equipment; I72: Traffic and Transport Planning; I90: Vehicles;
Filing Info
- Accession Number: 01374327
- Record Type: Publication
- Files: TRIS
- Created Date: Jun 28 2012 3:25PM