Characteristics of operating mode distributions of light duty vehicles by road type, average speed, and driver type for estimating on-road emissions: Case study of Beijing

References

• Abou-Senna, H., & Radwan, E. (2013). VISSIM/MOVES integration to investigate the effect of major key parameters on CO2 emissions. Transportation Research Part D: Transport and Environment, 21, 39–46. doi:10.1016/j.trd.2013.02.003
   Web of Science ®Google Scholar
• Abou-Senna, H., Radwan, E., Westerlund, K., & Cooper, C. D. (2013). Using a traffic simulation model (VISSIM) with an emissions model (MOVES) to predict emissions from vehicles on a limited-access highway. Journal of the Air & Waste Management Association, 63(7), 819–831. doi:10.1080/10962247.2013.795918
   Web of Science ®Google Scholar
• Alam, A., & Hatzopoulou, M. (2016). Deriving local operating distributions to estimate transit bus emissions across an urban network. Transportation Research Record: Journal of the Transportation Research Board, 2570, 57–68. doi:10.3141/2570-07
   Google Scholar
• Bandeira, J., Almeida, T. G., Khattak, A. J., Rouphail, N. M., & Coelho, M. C. (2013). Generating emissions information for route selection: Experimental monitoring and routes characterization. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 17(1), 3–17. doi:10.1080/15472450.2012.706197
   Web of Science ®Google Scholar
• Bar-Gera, H. (2007). Evaluation of a cellular phone-based system for measurements of traffic speeds and travel times: A case study from Israel. Transportation Research Part C: Emerging Technologies, 15(6), 380–391. doi:10.1016/j.trc.2007.06.003
   Web of Science ®Google Scholar
• Boriboonsomsin, K., Barth, M., & Vu, A. (2011). Evaluation of driving behavior and attitude towards eco-driving: A Southern California limited case study. Paper presented at 90th Annual Meeting of the Transportation Research Board, Washington DC.
   Google Scholar
• Chamberlin, R., Holmén, B. A., Talbot, E., & Sentoff, K. (2013). Comparative analysis of the EPA operating mode generator with real world operating mode data. Paper presented at 92nd Annual Meeting of the Transportation Research Board, Washington DC.
   Google Scholar
• Frey, H., Rouphail, N., & Zhai, H. (2006). Speed- and facility-specific emission estimates for on-road light-duty vehicles on the basis of real-world speed profiles. Transportation Research Record: Journal of the Transportation Research Board, 1987, 128–137. doi:10.3141/1987-14
   Google Scholar
• Jiménez-Palacios, J. L. (1999). Understanding and quantifying motor vehicle emissions with vehicle specific power and TILDAS remote sensing (PhD thesis). Massachusetts Institute of Technology, Cambridge, MA.
   Google Scholar
• Li, Z., Yu, L., Chitturi, M., Bill, A., & Noyce, D. (2015). Improved method for real-time vehicle emission estimation at intersections based on radar sensor data and MOVES model. Paper presented at 94th Annual Meeting of the Transportation Research Board, Washington DC.
   Google Scholar
• Li, C., Yu, L., He, W., Cheng, Y., & Song, G. (2017). Development of local emissions rate model for light-duty gasoline vehicles. Transportation Research Record: Journal of the Transportation Research Board, 2627, 67–76. doi:10.3141/2627-08
   Google Scholar
• Li, Z., Yu, L., Qiao, F., & Wang, J. (2015). Feasibility study of using driving simulator to develop operating mode distributions for emission analysis. Paper presented at 94th Annual Meeting of the Transportation Research Board, Washington DC.
   Google Scholar
• Qi, Y., Padiath, A., Zhao, Q., & Yu, L. (2016). Development of operating mode distributions for different types of roadways under different congestion levels for vehicle emission assessment using MOVES. Journal of the Air & Waste Management Association, 66(10), 1003–1011. doi:10.1080/10962247.2016.1194338
   PubMed Web of Science ®Google Scholar
• Smit, R., Ntziachristos, L., & Boulter, P. (2010). Validation of road vehicle and traffic emission models–A review and meta-analysis. Atmospheric Environment, 44(25), 2943–2953. doi:10.1016/j.atmosenv.2010.05.022
   Web of Science ®Google Scholar
• Song, G., & Yu, L. (2009). Estimation of fuel efficiency of road traffic by characterization of vehicle-specific power and speed based on floating car data. Transportation Research Record: Journal of the Transportation Research Board, 2139(1), 11–20. doi:10.3141/2139-02
   Google Scholar
• Song, G., & Yu, L. (2011). Characteristics of low-speed vehicle-specific power distributions on urban restricted-access roadways in Beijing. Transportation Research Record: Journal of the Transportation Research Board, 2233(1), 90–98. doi:10.3141/2233-11
   Google Scholar
• Song, G., Yu, L., & Geng, Z. (2015). Optimization of Wiedemann and Fritzsche car-following models for emission estimation. Transportation Research Part D: Transport and Environment, 34, 318–329. doi:10.1016/j.trd.2014.11.023
   Web of Science ®Google Scholar
• Song, G., Yu, L., & Tu, Z. (2012). Distribution characteristics of vehicle-specific power on urban restricted-access roadways. Journal of Transportation Engineering, 138(2), 202–209. doi:10.1061/(ASCE)TE.1943-5436.0000318
   Google Scholar
• Sun, X., Chen, X., Qi, Y., Mao, B., Yu, L., & Tang, P. (2016). Analyzing the effects of different advanced traffic signal status warning systems on vehicle emission reductions at signalized intersections. Paper presented at 95th Annual Meeting of the Transportation Research Board, Washington DC.
   Google Scholar
• Talbot, E., Chamberlin, R., Holmén, B. A., & Sentoff, K. (2014). Calibrating a traffic microsimulation model to real-world operating mode distributions. Paper presented at 93rd Annual Meeting of the Transportation Research Board, Washington DC.
   Google Scholar
• U.S. EPA. (2015). Exhaust emission rates for light-duty on-road vehicles in MOVES2014. EPA-420-R-15-005. https://nepis.epa.gov/Exe/ZyPDF.cgi/P100NNVN.PDF?Dockey=P100NNVN.PDF
   Google Scholar
• U.S. EPA (2016a). Using MOVES for estimating state and local inventories of onroad greenhouse gas emissions and energy consumption. EPA-420-B-16-059. https://nepis.epa.gov/Exe/ZyPDF.cgi/P100OW0B.PDF?Dockey=P100OW0B.PDF
   Google Scholar
• U.S. EPA. (2016b). Population and activity of on-road vehicles in MOVES2014. EPA-420-R-16-003a. https://nepis.epa.gov/Exe/ZyPDF.cgi/P100O7PS.PDF?Dockey=P100O7PS.PDF
   Google Scholar
• Wang, J., Rakha, H. A., & Fadhloun, K. (2017). Comparison of car-following models: A vehicle fuel consumption and emissions estimation perspective. Paper presented at 96th Annual Meeting of the Transportation Research Board, Washington DC.
   Google Scholar
• Wang, J., Rakha, Hesham, A., & Yu, L. (2015). Operating mode distribution characteristics of different freeway weaving configurations and their effects on vehicular emissions. Paper presented at 94th Annual Meeting of the Transportation Research Board, Washington DC.
   Google Scholar
• Wei, H., Liu, H., Lu, M., & Yi, P. (2012). Integrating traffic operation with emission impact using dual-loop data. Ohio Transportation Consortium (OTC) Research Project Report, University of Cincinnati. Retrieved from: http://otc.uakron.edu/files/2014/02/Integrating-Traffic-Operation-with-Emission-Impact-using-Dual-loop-Data.pdf
   Google Scholar
• Xie, Y., Chowdhury, M., Bhavsar, P., & Zhou, Y. (2012). An integrated modeling approach for facilitating emission estimations of alternative fueled vehicles. Transportation Research Part D: Transport and Environment, 17(1), 15–20. doi:10.1016/j.trd.2011.08.009
   Web of Science ®Google Scholar
• Xu, X., Liu, H., Anderson, J. M., Xu, Y., Hunter, M. P., Rodgers, M. O., & Guensler, R. L. (2016). Estimating project-level vehicle emissions with Vissim and MOVES-matrix. Transportation Research Record: Journal of the Transportation Research Board, 2570, 107–117. doi:10.3141/2570-12
   Google Scholar
• Yang, Z., Wang, H., Shao, Z., & Muncrief, R. (2015). Review of Beijing’s comprehensive motor vehicle emission control programs. Retrieved from: http://www.theicct.org/sites/default/files/publications/Beijing_Emission_Control_Programs_201511.pdf
   Google Scholar
• Yao, Z., Wei, H., Li, Z., Ma, T., Liu, H., & Yang, Y. (2013). Developing operating mode distribution inputs for MOVES with a computer vision-based vehicle data collector. Transportation Research Record: Journal of the Transportation Research Board, 2340(1), 49–58. doi:10.3141/2340-06
   Google Scholar
• Yu, L., & Tao, F. (2012). Effects of driving behaviors on vehicle emissions: Case study in Houston, Texas. Paper presented at 91st Annual Meeting of the Transportation Research Board, Washington DC.[TQ13]
   Google Scholar
• Zhai, Z., Song, G., Lu, H., He, W., & Yu, L. (2017). Validation of temporal and spatial consistency of facility- and speed-specific vehicle-specific power distributions for emission estimation: A case study in Beijing, China. Journal of the Air & Waste Management Association, 67(9), 949–957. doi:10.1080/10962247.2017.1294545
   PubMed Web of Science ®Google Scholar
• Zhao, X., Wu, Y., Rong, J., & Zhang, Y. (2015). Development of a driving simulator based eco-driving support system. Transportation Research Part C: Emerging Technologies, 58, 631–641. doi:10.1016/j.trc.2015.03.030
   Web of Science ®Google Scholar
• Zhao, Q., Yu, L., & Song, G. (2012). Characteristics of VSP distributions of light-duty and heavy-duty vehicles on freeway: A case study. Paper presented at 91st Annual Meeting of the Transportation Research Board, Washington DC.
   Google Scholar