Open Access
| Issue |
E3S Web Conf.
Volume 310, 2021
Annual International Scientific Conference “Spatial Data: Science, Research and Technology 2021”
|
|
|---|---|---|
| Article Number | 03004 | |
| Number of page(s) | 8 | |
| Section | Geodesy. Navigation. GLONASS - GNSS | |
| DOI | https://doi.org/10.1051/e3sconf/202131003004 | |
| Published online | 15 October 2021 | |
- Murphey, Y.L., R. Milton, and L. Kiliaris. Driver’s style classification using jerk analysis. in 2009 IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems (2009) [Google Scholar]
- Ferreira, J.C., J.d. Almeida, and A.R.D. Silva, The Impact of Driving Styles on Fuel Consumption: A Data-Warehouse-and-Data-Mining-Based Discovery Process. IEEE Transactions on Intelligent Transportation Systems, 16(5): p. 2653-2662 (2015) [CrossRef] [Google Scholar]
- Zheng, F., et al., Influence of driver characteristics on emissions and fuel consumption. Transportation Research Procedia, 27: p. 624-63 (2017) [CrossRef] [Google Scholar]
- Feng, Y., et al. Driving Style Analysis by Classifying Real-World Data with Support Vector Clustering, in 2018 3rd IEEE International Conference on Intelligent Transportation Engineering (ICITE) (2018) [Google Scholar]
- Zhao, Y., T. Yamamoto, and T. Morikawa, An analysis on older driver’s driving behavior by GPS tracking data: Road selection, left/right turn, and driving speed. Journal of traffic and transportation engineering (English edition), 5(1): p. 56-65 (2018) [CrossRef] [Google Scholar]
- Ceikute, V. and C.S. Jensen. Routing service quality local driver behavior versus routing services, in 2013 IEEE 14th International Conference on Mobile Data Management. IEEE (2013) [Google Scholar]
- Berry, I.M., The effects of driving style and vehicle performance on the real-world fuel consumption of US light-duty vehicles, Massachusetts Institute of Technology (2010) [Google Scholar]
- Beusen, B., et al., Using on-board logging devices to study the longer-term impact of an eco-driving course. Transportation research part D: transport and environment, 14(7): p. 514-520 (2009) [CrossRef] [Google Scholar]
- Website: http://www.alhadeedtechnologies.com/howitworks.html. [Google Scholar]
- (Vietnam), M.o.T., National technical regulation on vehicle tracking equipment, Ministry of Transport of Vietnam, Vietnam (2014) [Google Scholar]
- (Vietnam), M.o.T., Regulation on provision, management and use of data from vehicle tracking device. September, Ministry of Transport of Vietnam, Vietnam (2015) [Google Scholar]
- Ding, X., et al., An improved Thevenin model of lithium-ion battery with high accuracy for electric vehicles. Applied Energy, 254: p. 113615 (2015) [Google Scholar]
- Maia, R., et al. Electric vehicle simulator for energy consumption studies in electric mobility systems. in 2011 IEEE Forum on Integrated and Sustainable Transportation Systems, IEEE (2011) [Google Scholar]
- Peterson, S.B., J. Apt, and J. Whitacre, Lithium-ion battery cell degradation resulting from realistic vehicle and vehicle-to-grid utilization. Journal of Power Sources, 195(8): p. 2385-2392 (2010) [CrossRef] [Google Scholar]
- Manual, H.C., Highway capacity manual. Washington, DC, 2 (2000) [Google Scholar]
- Barth, M., et al., The development of a comprehensive modal emissions model, ‖ NCHRP Web-Only Document 122. Contractor’s final report for NCHRP Project, 2000: p. 25-11 (2000) [Google Scholar]
- Jimenez-Palacios, J.L., Understanding and quantifying motor vehicle emissions with vehicle specific power and TILDAS remote sensing. Massachusetts Institute of Technology, 1998. [Google Scholar]
- Scora, G. and M. Barth, Comprehensive modal emissions model (cmem), version 3.01. User guide. Centre for environmental research and technology. University of California, Riverside, 1070 (2006) [Google Scholar]
- Song, G., L. Yu, and Z. Geng, Optimization of Wiedemann and Fritzsche car-following models for emission estimation. Transportation Research Part D: Transport and Environment, 34: p. 318-329 (2015) [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.