Long short-term memory network-based emission models for conventional and new energy buses

References

• Agar, B. J., Baetz, B. W., & Wilson, B. G. (2007). Fuel consumption, emissions estimation, and emissions cost estimates using global positioning data. Journal of the Air & Waste Management Association, 57(3), 348–354. doi:10.1080/10473289.2007.10465328
   Web of Science ®Google Scholar
• Argonne National Laboratory. (2019). Autonomie home page, Retrieved from https://www.autonomie.net/expertise/autonomie.html.
   Google Scholar
• Aziz, H. M. A., & Ukkusuri, S. V. (2018). A novel approach to estimate emissions from large transportation networks: Hierarchical clustering-based link-driving-schedules for EPA-MOVES using dynamic time warping measures. International Journal of Sustainable Transportation, 12(3), 192–204. doi:10.1080/15568318.2017.1346732
   Web of Science ®Google Scholar
• Baidu, B. (2015). Report on nutrition and chronic disease status of Chinese residents, Retrieved from https://baike.baidu.com/item/%E4%B8%AD%E5%9B%BD%E5%B1%85%E6%B0%91%E8%90%A5%E5%85%BB%E4%B8%8E%E6%85%A2%E6%80%A7%E7%97%85%E7%8A%B6%E5%86%B5%E6%8A%A5%E5%91%8A(2015)/17945451.
   Google Scholar
• Bakker, S., & Konings, R. (2018). The transition to zero-emission buses in public transport—The need for institutional innovation. Transportation Research Part D: Transport and Environment, 64, 204–215. doi:10.1016/j.trd.2017.08.023
   Web of Science ®Google Scholar
• Bermond, V., Caliman, R. R., Rosa, R. D. A., Pereira, E. P., & Carvalhaes, B. B. (2019). A method to assess the eco-efficiency of a public bus transportation service. International Journal of Sustainable Transportation, 13(8), 567–581. doi:10.1080/15568318.2018
   PubMed Web of Science ®Google Scholar
• Bigazzi, A. Y., Figliozzi, M. A., & Clifton, K. J. (2015). Traffic congestion and air pollution exposure for motorists: Comparing exposure duration and intensity. International Journal of Sustainable Transportation, 9(7), 443–456. doi:10.1080/15568318.2013.805345
   Web of Science ®Google Scholar
• Börjesson, M., Hamilton, C. J., Näsman, P., & Papaix, C. (2015). Factors driving public support for road congestion reduction policies: Congestion charging, free public transport and more roads in Stockholm, Helsinki and Lyon. Transportation Research Part A: Policy and Practice, 78, 452–462. doi:10.1016/j.tra.2015.06.008
   Web of Science ®Google Scholar
• Brooker, A., Gonder, J., Wang, L., Wood, E., & Ramroth, L. (2015). Fastsim: A model to estimate vehicle efficiency, cost and performance. SAE Technical Papers, 2015.
   Google Scholar
• Corazza, M. V., Guida, U., Musso, A., & Tozzi, M. (2016). A European vision for more environmentally friendly buses. Transportation Research Part D: Transport and Environment, 45, 48–63. doi:10.1016/j.trd.2015.04.001
   Web of Science ®Google Scholar
• EPA. (2014). MOVES 2014a user guide. US: US Environmental Protection Agency.
   Google Scholar
• Feng, A., 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. doi:10.3141/1587-07
   Google Scholar
• Hallmark, S. L., Wang, B., & Sperry, R. (2013). Comparison of on-road emissions for hybrid and regular transit buses. Journal of the Air & Waste Management Association, 63(10), 1212. doi:10.1080/10962247.2013.813874
   Web of Science ®Google Scholar
• Hashemi, N., & Clark, N. N. (2007). Artificial neural network as a predictive tool for emissions from heavy-duty diesel vehicles in Southern California. International Journal of Engine Research, 8(4), 321–336. doi:10.1243/14680874JER00807
   Web of Science ®Google Scholar
• Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. doi:10.1162/neco.1997.9.8.1735
   PubMed Web of Science ®Google Scholar
• Informatik, F. F., Bengio, Y., Frasconi, P., & Schmidhuber, J. (2001). Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies. In S. C. Kremer & J. F. Kolen (Eds.), A Field Guide to Dynamical Recurrent Neural Networks. Piscataway, N.J.: IEEE press.
   Google Scholar
• Ji, Y., Fan, Y., Ermagun, A., Cao, X., Wang, W., & Das, K. (2017). Public bicycle as a feeder mode to rail transit in China: the role of gender, age, income, trip purpose, and bicycle theft experience. International Journal of Sustainable Transportation, 11(4), 308–317. doi:10.1080/15568318.2016.1253802
   Web of Science ®Google Scholar
• Jiménez-Palacios, J. L. (1999). Understanding and quantifying motor vehicle emissions with vehicle specific power and TILDAS remote sensing (Unpublished doctorial dissertation). Massachusetts Institute of Technology, Massachusetts, US.
   Google Scholar
• Li, J. (2016). Battery-electric transit bus developments and operations: A review. International Journal of Sustainable Transportation, 10(3), 157–169. doi:10.1080/15568318.2013.872737
   Web of Science ®Google Scholar
• Li, J., Gupta, S. D., Zhang, L., Zhou, K., & Zhang, W. (2012). Evaluate bus emissions generated near far-side and near-side stops and potential reductions by ITS: An empirical study. Transportation Research Part D: Transport and Environment, 17(1), 73–77. doi:10.1016/j.trd.2011.09.012
   Web of Science ®Google Scholar
• Liu, P., Wu, J., Zhou, H., Bao, J., & Yang, Z. (2019). Estimating queue length for contraflow left-turn lane design at signalized intersections. Journal of Transportation Engineering. Part A. Systems, 145(6), 4019020.
   Web of Science ®Google Scholar
• Mathai, R., Malhotra, R. K., Subramanian, K. A., & Das, L. M. (2012). Comparative evaluation of performance, emission, lubricant and deposit characteristics of spark ignition engine fueled with CNG and 18% hydrogen-CNG. International Journal of Hydrogen Energy, 37(8), 6893–6900. doi:10.1016/j.ijhydene.2012.01.083
   Web of Science ®Google Scholar
• Ntziachristos, L., Gkatzoflias, D., Kouridis, C., & Samaras, Z. (2009). COPERT: A European road transport emission inventory model. In: Information technologies in environmental engineering (pp. 491–504). Berlin, Heidelberg: Springer.
   Google Scholar
• Pan, Y., Chen, S., Qiao, F., Ukkusuri, S. V., & Tang, K. (2019). Estimation of real-driving emissions for buses fueled with liquefied natural gas based on gradient boosted regression trees. Science of the Total Environment, 660, 741–750. doi:10.1016/j.scitotenv.2019.01.054
   PubMed Web of Science ®Google Scholar
• Perugu, H. (2018). Emission modelling of light-duty vehicles in India using the revamped VSP-based MOVES model: The case study of Hyderabad. Transportation Research Part D: Transport and Environment, 68, 150–163. doi:10.1016/j.trd.2018.01.031
   Web of Science ®Google Scholar
• Pradono, P., Kusumantoro, I. P., & Retapradana, A. (2015). Towards carbon dioxide emission reduction through public transport revitalization: Case of proposed Surabaya Tram transit project. Journal of the Eastern Asia Society for Transportation Studies, 11, 967–978.
   Google Scholar
• Qi, Y. G., Teng, H. H., & Yu, L. (2004). Microscale emission models incorporating acceleration and deceleration. Journal of Transportation Engineering, 130(3), 348–359. doi:10.1061/(ASCE)0733-947X(2004)130:3(348)
   Google Scholar
• Rakha, H., Ahn, K., & Trani, A. (2003). Comparison of MOBILE5a, MOBILE6, VT-MICRO, and CMEM models for estimating hot-stabilized light-duty gasoline vehicle emissions. Canadian Journal of Civil Engineering, 30(6), 1010–1021. doi:10.1139/l03-017
   Web of Science ®Google Scholar
• 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. doi:10.1016/S1361-9209(03)00054-3
   Web of Science ®Google Scholar
• Rakha, H., Yue, H., & Dion, F. (2011). VT-meso model framework for estimating hot-stabilized light-duty vehicle fuel consumption and emission rates. Canadian Journal of Civil Engineering, 38(11), 1274–1286. doi:10.1139/l11-086
   Web of Science ®Google Scholar
• Schäfer, F., & van Basshuysen, R. (1995). The influence of fuel and lubricants on emissions and fuel consumption reduced emissions and fuel consumption in automobile engines. Vienna: Springer.
   Google Scholar
• Scora, G., & Barth, M. (2006). Comprehensive modal emissions model (CMEM), version 3.01 user’s guide. Riverside, CA: University of California.
   Google Scholar
• So, J. J., Motamedidehkordi, N., Wu, Y., Busch, F., & Choi, K. (2018). Estimating emissions based on the integration of microscopic traffic simulation and vehicle dynamics model. International Journal of Sustainable Transportation, 12(4), 286–298. doi:10.1080/15568318.2017.1363328
   Web of Science ®Google Scholar
• Tóth-Nagy, C., Conley, J. J., Jarrett, R. P., & Clark, N. N. (2006). Further validation of artificial neural network-based emissions simulation models for conventional and hybrid electric vehicles. Journal of the Air & Waste Management Association, 56(7), 898–910. doi:10.1080/10473289.2006.10464513
   Web of Science ®Google Scholar
• Treiber, M., & Kesting, A. (2008). How much does traffic congestion increase fuel consumption and emissions? Applying a fuel consumption model to the NGSIM trajectory data. Transportation Research Board 87th Annual Meeting.
   Google Scholar
• Vincent, W., & Jerram, L. C. (2006). The potential for bus rapid transit to reduce transportation-related CO2 emissions. Journal of Public Transportation, 9(3), 219–237. doi:10.5038/2375-0901.9.3.12
   Google Scholar
• Wang, C., Ye, Z., Yu, Y., & Gong, W. (2018). Estimation of bus emission models for different fuel types of buses under real conditions. Science of the Total Environment, 640–641, 965–972. doi:10.1016/j.scitotenv.2018.05.289
   PubMed Web of Science ®Google Scholar
• Wang, C., Ye, Z., Chen, E., Xu, M., & Wang, W. (2019). Diffusion approximation for exploring the correlation between failure rate and bus-stop operation. Transportmetrica A: Transport Science, 15(2), 1306–1320. doi:10.1080/23249935.2019.1594445
   Web of Science ®Google Scholar
• Wang, G., Bai, S., & Ogden, J. M. (2009). Identifying contributions of on-road motor vehicles to urban air pollution using travel demand model data. Transportation Research Part D: Transport and Environment, 14(3), 168–179. doi:10.1016/j.trd.2008.11.011
   Web of Science ®Google Scholar
• Werbos, P. J. (1988). Generalization of backpropagation with application to a recurrent gas market model. Neural Networks, 1(4), 339–356. doi:10.1016/0893-6080(88)90007-X
   Web of Science ®Google Scholar
• Xu, Y., Lee, D. Y., Gbologah, F., Cernjul, G., Elango, V., & Rodgers, M. (2013). Load-based life cycle greenhouse gas emissions calculator for transit buses: An Atlanta, GA, case study. Conference on Green Streets. doi:10.1061/9780784413197.023
   Google Scholar
• Yu, Q., Li, T., & Li, H. (2016). Improving urban bus emission and fuel consumption modeling by incorporating passenger load factor for real world driving. Applied Energy, 161, 101–111. doi:10.1016/j.apenergy.2015.09.096
   Web of Science ®Google Scholar
• Yu, Q., & Li, T. (2014). Evaluation of bus emissions generated near bus stops. Atmospheric Environment, 85(2), 195–203. doi:10.1016/j.atmosenv.2013.12.020
   Google Scholar
• Yuan, Y., Yang, M., Wu, J., Rasouli, S., & Lei, D. (2019). Assessing bus transit service from the perspective of elderly passengers in Harbin, China. International Journal of Sustainable Transportation, 13(10), 761–776. doi:10.1080/15568318.2018.1512691
   Web of Science ®Google Scholar
• Yue, H. (2008). Mesoscopic fuel consumption and emission modeling (Unpublished doctorial dissertation). Virginia Polytechnic Institute and State University, Virginia, US.
   Google Scholar
• Zhai, H., Frey, H. C., & Rouphail, N. M. (2008). Regional on-road mobile source emissions characterization for conventional and alternative vehicle technologies. Environmental Science & Technology, 42(21), 7985–7991.
   PubMed Web of Science ®Google Scholar