This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Developing a Real-World, Second-by-Second Driving Cycle Database through Public Vehicle Trip Surveys
Technical Paper
2019-01-5074
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Event:
Automotive Technical Papers
Language:
English
Abstract
Real-world second-by-second vehicle driving cycle data is very important for vehicle research and development. A project solely dedicated to generating such information would be tremendously costly and time consuming. Alternatively, we developed such a database by utilizing two publicly available passenger vehicle travel surveys: 2004-2006 Puget Sound Regional Council (PSRC) Travel Survey and 2011 Atlanta Regional Commission (ARC) Travel Survey. The surveys complement each other - the former is in low time resolution but covers driver operation for over one year whereas the latter is in high time resolution but represents only one-week-long driving operation. After analyzing the PSRC survey, we chose 382 vehicles, each of which continuously operated for one year, and matched their trips to all the ARC trips. The matching is carried out based on trip distance first, then on average speed, and finally on duration. Of the total 509,158 trips made by the 382 PSRC vehicles, 496,276 trips (97.47%) were successfully matched to single original ARC trips. The remaining trips were matched to either ARC sub-trips or combined ARC trips. The resulting high-resolution year-long database can be used by drive cycle analysis tools such as the advanced vehicle simulator ADVISORTM to investigate fuel economy, battery life, and vehicle emissions under various conditions. Our approach can be employed to produce other realistic databases from publicly available vehicle travel surveys.
Recommended Content
Technical Paper | Chassis Design for a Small Electric City Car |
Technical Paper | Changing Habits to Improve Fuel Economy |
Authors
Topic
Citation
Khemri, N., Supina, J., Syed, F., and Ying, H., "Developing a Real-World, Second-by-Second Driving Cycle Database through Public Vehicle Trip Surveys," SAE Technical Paper 2019-01-5074, 2019, https://doi.org/10.4271/2019-01-5074.Data Sets - Support Documents
Title | Description | Download |
---|---|---|
Unnamed Dataset 1 | ||
Unnamed Dataset 2 | ||
Unnamed Dataset 3 |
Also In
References
- A Review of Consumer Benefits from Corporate Average Fuel Economy (CAFE) Standards 2013 http://consumersunion.org/wp-content/uploads/2013/06/FuelEconomyStandards.pdf
- Posada , F. , and German , J. 2013
- Gondor , J. , Markel , T. , Simpson , A. , and Thornton , M. 2007
- Green , D. 2010
- Ribeiro , M. , LarraƱaga , A. , Arellana , J. , and Cybis , H. Influence of GPS and Self-Reported Data in Travel Demand Models Procedia - Social and Behavioral Sciences 162 467 476 2014
- EPA.gov Fuel Economy Label Updates 2014 https://www.epa.gov/recalls/fuel-economy-label-updates 2016
- Dill , J. , Broach , J. , Deutsch-Burgne , K. , Xu , Y. et al. 2014
- Jun , M. and Xiangyin , L. MPG Prediction Based on BP Neural Network 1st IEEE Conference on Industrial Electronics and Applications Singapore 2006 1 3
- Rusiman , M. , Nasibov , E. , and Adnan , R. The Optimal Fuzzy c-Regression Models (OFCRM) in Miles per Gallon of Cars Prediction IEEE Student Conference, Research and Development Cyberjaya, Malaysia 2011 333 338
- Slavin , D. , Abou-Nasr , M. , Filev , D. , and Kolmanovsky , L. Empirical Modeling of Vehicle Fuel Economy Based on Historical Data IEEE International Joint Conference on Neural Networks Dallas, TX 2013 1 6
- Wu , J. and Liu , J. A Forecasting System for Car Fuel Consumption Using a Radial Basis Function Neural Network Expert Systems with Applications 39 2 1883 1888 2012
- Moawad , A. , Singh , G. , Hagspiel , S. , Fellah , M. et al. Impact of Real World Drive Cycles on PHEV Fuel Efficiency and Cost for Different Powertrain and Battery Characteristics 24th International Electric Vehicle Symposium and Exposition Stavanger, Norway 2009
- Liu , J. , Wang , X. , and Khattak , A. Customizing Driving Cycles to Support Vehicle Purchase and Use Decisions: Fuel Economy Estimation for Alternative Fuel Transportation Research Part C: Emerging Technologies 67 280 298 2016
- Earleywine , M. , Gonder , J. , Markel , T. , and Thornton , M. Simulated Fuel Economy and Performance of Advanced Hybrid Electric and Plug-In Hybrid Electric Vehicles Using In-Use Travel Profiles IEEE Vehicle Power and Propulsion Conference Lille, France 2010 1 6
- Gonder , J. , Earleywine , M. , and Sparks , W. Analyzing Vehicle Fuel Saving Opportunities through Intelligent Driver Feedback SAE Int. J. Passenger Cars - Electron. Electr. Syst. 5 2 450 461 2012 10.4271/2012-01-0494
- Pati , R. , Adornato , B. , and Filipi , Z. Impact of Naturalistic Driving Patterns on PHEV Performance and System Design SAE Technical Paper 2009-01-2715 2009 10.4271/2009-01-2715
- Klier , T. , Linn , J. , and Zhou , Y. 2016
- Nyhan , M. , Sobolevsky , S. , Kang , C. , Robinson , P. et al. Predicting Vehicular Emissions in High Spatial Resolution Using Pervasively Measured Transportation Data and Microscopic Emissions Model Atmospheric Environment 140 352 363 2016
- Maness , H. , Thurlow , M. , McDonald , B. , and Harley , R. Estimates of CO 2 Traffic Emissions from Mobile Concentration Measurements Journal of Geophys Research: Atmospheres 120 2087 2102 2015
- Jean , J. , Bachman , W. , Oliveira , M. , Auld , J. et al. 2014
- Doherty , S. , Noel , N. , Gosselin , M. , Sirois , C. et al. 2001 449 466
- Transportation Secure Data Center 2017 www.nrel.gov/tsdc
- Hong , J. Non-Linear Influences of the Built Environment on Transportation Emissions: Focusing on Densities The Journal of Transport and Land Use 10 1 229 240 2017
- Liu , J. , Wang , X. , and Khattak , A. Generating Real-Time Driving Volatility Information World Congress on Intelligent Transport Systems Detroit, MI 2014
- Khemri , N. , Ying , H. , Supina , J. , and Syed , F. Utilizing Public Vehicle Travel Survey Datasets for Vehicle Driving Pattern and Fuel Economy Studies SAE Technical Paper 2017-01-0232 2017 10.4271/2017-01-0232
- Puget Sound Regional Council 2008
- Livingston , J. 2011
- Consumer Reports Magazine 2018 38 46