References
- An, F., Barth, M., Norbeck, J., & Ross, M. (1997). Development of comprehensive modal emissions model: Operating under hot-stabilized conditions. Transportation Research Record: Journal of the Transportation Research Board, 1587(1), 52–62. https://doi.org/10.3141/1587-07
- Barth, M., An, F., Norbeck, J., & Ross, M. (1996). Modal emissions modelling: A physical approach. Transportation Research Record: Journal of the Transportation Research Board, 1520(1), 81–88. https://doi.org/10.1177/0361198196152000110
- Cappiello, A., Chabini, I., Nam, E., & Lue, A. (2002). A statistical model of vehicle emissions and fuel consumption. Proceedings of the IEEE International Conference on Intelligent Transportation Systems, 2002, 801–809.
- Duarte, G., Gonçalves, G., Baptista, P., & Farias, T. (2015). Establishing bonds between vehicle certification data and real-world vehicle fuel consumption - A vehicle specific power approach. Energy Conversion and Management, 92, 251–265. https://doi.org/10.1016/j.enconman.2014.12.042
- Feng, F., Bao, S., Sayer, J. R., Flannagan, C., Manser, M., & Wunderlich, R. (2017). Can vehicle longitudinal jerk be used to identify aggressive drivers? An examination using naturalistic driving data. Accident; Analysis and Prevention, 104, 125–136.
- IEA. (2013). CO2 emissions from fuel combustion 2013.
- Jiménez-Palacios, J. L. (1999). Understanding and quantifying motor vehicle emissions with vehicle specific power and TILDAS remote sensing [doctoral thesis, Massachusetts Institute of Technology]. https://dspace.mit.edu/handle/1721.1/44505
- Kamrani, M., Arvin, M., & Khattak, A. J. (2018). Extracting useful information from basic safety message data: an empirical study of driving volatility measures and crash frequency at intersections. Transportation Research Record: Journal of the Transportation Research Board, 2672(38), 290–301. https://doi.org/10.1177/0361198118773869
- Khattak, A. J., & Wali, B. (2017). Analysis of volatility in driving regimes extracted from basic safety messages transmitted between connected vehicles. Transportation Research Part C: Emerging Technologies, 84c, 48–73.
- Li, X., Ghiasi, A., Xu, Z., & Qu, X. (2018). A piecewise trajectory optimization model for connected automated vehicles: Exact optimization algorithm and queue propagation analysis. Transportation Research Part B: Methodological, 118(12), 429–456. https://doi.org/10.1016/j.trb.2018.11.002
- Liu, Z., Ivanco, A., & Filipi, Z. (2015). Quantification of drive cycle's rapid speed fluctuations using Fourier analysis. SAE International Journal of Alternative Powertrains, 4(1), 170–177. https://doi.org/10.4271/2015-01-1213
- Murphey, Y. L., Milton, R., & Kiliaris, L. (2009). Driver's style classification using jerk analysis. In IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems (CIVVS '09). IEEE.
- Organization of the Petroleum Exporting Countries. ( 2014). 2014 world oil outlook. Retrieved February 20, 2015, from http://www.opec.org/opec_web/static_files_project/media/downloads/publications/WOO_2014.pdf
- Rakha, H., Ahn, K., & Trani, A. (2004). Development of VT-micro model for estimating hot stabilized light duty vehicle and truck emissions. Transportation Research Part D: Transport and Environment, 9(1), 49–74. https://doi.org/10.1016/S1361-9209(03)00054-3
- Toledo, G., & Shiftan, Y. (2016). Can feedback from in-vehicle data recorders improve driver behavior and reduce fuel consumption? Transportation Research Part A: Policy & Practice, 94, 194–204.
- U.S. Environmental Protection Agency (EPA). (2015). Greenhouse gas inventory report: 1990–2014.
- Wang, X., Khattak, A. J., Liu, J., Masghati-Amoli, G., & Son, S. (2015). What is the level of volatility in instantaneous driving decisions? Transportation Research Part C: Emerging Technologies, 58, 413–427. https://doi.org/10.1016/j.trc.2014.12.014
- Xu, Z., Wang, Y., Wang, G., Li, X., Bertini, R. L., Qu, X., & Zhao, X. (2021). Trajectory optimization for a connected automated traffic stream: Comparison between exact model and fast heuristics. IEEE Transactions on Intelligent Transportation Systems, 22(5), 2969–2978. https://doi.org/10.1109/TITS.2020.2978382
- Xu, Z., Wei, T., Easa, S., Zhao, X., & Qu, X. (2018). Modelling relationship between truck fuel consumption and driving behaviour using data from internet of vehicles. Computer-Aided Civil and Infrastructure Engineering, 33(3), 209–219. https://doi.org/10.1111/mice.12344
- Zhou, M., & Jin, H. (2017). Development of a transient fuel consumption model. Transportation Research Part D: Transport and Environment, 51, 82–93. https://doi.org/10.1016/j.trd.2016.12.001