Microscopic Emission and Fuel Consumption Modeling for Light-duty Vehicles Using Portable Emission Measurement System Data
Microscopic emission and fuel consumption models
have been widely recognized as an effective method to quantify real
traffic emission and energy consumption when they are applied with
microscopic traffic simulation models. This paper presents a
framework for developing the Microscopic Emission (HC, CO, NOx,
and CO2) and Fuel consumption (MEF) models for light-duty
vehicles. The variable of composite acceleration is introduced into
the MEF model with the purpose of capturing the effects of historical
accelerations interacting with current speed on emission and fuel
consumption. The MEF model is calibrated by multivariate
least-squares method for two types of light-duty vehicle using
on-board data collected in Beijing, China by a Portable Emission
Measurement System (PEMS). The instantaneous validation results
shows the MEF model performs better with lower Mean Absolute
Percentage Error (MAPE) compared to other two models. Moreover,
the aggregate validation results tells the MEF model produces
reasonable estimations compared to actual measurements with
prediction errors within 12%, 10%, 19%, and 9% for HC, CO, NOx
emissions and fuel consumption, respectively.
[1] EPA, "User-s Guide to MOBILE6.0. Mobile Source Emission Factor
Model". EPA-420-R-02-001. United States Environmental Protection
Agency, 2001.
[2] CARB, "EMFAC User-s Guide".U.S. California: California Air Resource
Board, 2002.
[3] C. Kouridis, L. Ntziachristos, and Z. Samaras, "COPERT III Computer
programme to calculate emissions from road transport". European
Environment Agency, 2000.
[4] M. Keller, and N. Kljun, "ARTEMIS Road Emission Model User Guide".
EU Commission, 2007.
[5] EPA, "Draft Motor Vehicle Emission Simulator (MOVES) 2009".
EPA-420-B-09-008. United States Environmental Protection Agency,
2009.
[6] M. Barth, F. An, T. Younglove, G. Scora, C. Levine, M. Ross, and T.
Wenzel, "Comprehensive Modal Emission Model (CMEM), version 2.0,
User-s Guide". University of California, Riverside, 2000.
[7] H. Kuhns, C. Mazzoleni, H. Moosmuller, D. Nikolc, R. Keislar, P. Barber,
L. Zheng, V. Etyemezian, and J.Watson, "Remote sensing of PM, NO, CO
and HC emission factors for on-road gasoline and diesel engine vehicles
in Las Vegas, NV". Science of the Total Environment, Vol. 322, No. 1-3,
pp. 123-137, 2004.
[8] N. Rogak, U. Pott, T. Dann, and D. Wang, "Gaseous emissions from
vehicles in a traffic tunnel in Vancouver, British Columbia". Air & Waste
Manage. Assoc, Vol. 48, No.7, pp.604-615, 1998.
[9] C. John, R. Friedrich, J. Staehelin, K. Schläpfer, and W.A. Stahel,
"Comparison of emission factors for road traffic from a tunnel study
(Gubrist tunnel, Switzerland) and from emission modeling". Atmospheric
Environment, Vol. 33, No. 20, pp. 3367-3376, 1999.
[10] F. Yang, and L.Yu, "A Microscopic Emission Model for the Light-Duty
Vehicles Based on PEMS Data". In Proceedings of International
Conference of Transportation Engineering 2007 (ICTE 2007), Chengdu,
China, 2007.
[11] D.A.M.M. Elst, R.T.M. Smokers, and J.W.De Koning, "Evaluation of the
Capabilities of On-Board Emission Measurement System for the Purpose
of Generating Real-Life Emission Factors". TNO Report
04.OR.VM.039.1/DE, 2004.
[12] G. Song, , L. Yu, and X. Zhang, "Emission Analysis at Toll Station Area
in Beijing with Portable Emission Measurement System". Transportation
Research Record: Journal of the Transportation Research Board, 2058,
pp.106-114, 2008.
[13] X. Li, G. Li, S. Pang, X. Yang, and J. Tian, "Signal timing of intersections
using integrated optimization of traffic quality, emissions and fuel
consumption: a note". Transportation Research Part D 9, Vol. 9, pp.
401-407, 2004.
[14] H. Liu, M. Barth, G. Scora, N. Davis, and J. Lents, "Using Portable
Emission Measurement System for Transportation Emissions Studies:
Comparison with Laboratory Methods". In Proceedings of 89th
Transportation Research Board Annual Meeting. USA, Washingtong
D.C., 2010.
[15] H. Rakha, K. Ahn, and A. Trani, "Development of VT-Micro model for
estimating hot stabilized light duty vehicle and truck emission".
Transportation Research Part D, Vol.9, pp.49-74, 2004.
[16] H. Teng, L. Yu, and Y. Qi, "Statistical micro-scale emission models
incorporating acceleration and deceleration". In Proceedings of 81th
Transportation Research Board Annual Meeting. USA , Washington D.C.,
2002.
[17] HORIBA, "On Board Emission Measurement System OBS-2200
Instruction Manual". HORIBA Ltd, 2005.
[18] Y. Kamarianakis, and H.O. Gao, "Accounting for exhaust gas transport
dynamics in instantaneous emission models via Smooth Transition
Regression". In Proceedings of 89th Transportation Research Board
Annual Meeting. USA Washington D.C., 2010.
[19] R.K. Jr, "Data smoothing using a least squares fit C++ class". ISA
Transactions, 37, pp.3-19, 1998.
[20] M. Barth, F. An, J. Norbeck, and M. Ross, "Modal Emissions Modeling:
A Physical Approach". Transportation Research Record: Journal of the
Transportation Research Board, 1520, pp.81-88, 1996.
[21] H. Guo, J. Zeng, and Y. Hu, "Neural Network Modeling of Vehicle Gross
Emitter Prediction Based on Remote Sensing Data". In Proceedings of the
2006 IEEE International Conference. Taipei, Taiwan, 2006
[1] EPA, "User-s Guide to MOBILE6.0. Mobile Source Emission Factor
Model". EPA-420-R-02-001. United States Environmental Protection
Agency, 2001.
[2] CARB, "EMFAC User-s Guide".U.S. California: California Air Resource
Board, 2002.
[3] C. Kouridis, L. Ntziachristos, and Z. Samaras, "COPERT III Computer
programme to calculate emissions from road transport". European
Environment Agency, 2000.
[4] M. Keller, and N. Kljun, "ARTEMIS Road Emission Model User Guide".
EU Commission, 2007.
[5] EPA, "Draft Motor Vehicle Emission Simulator (MOVES) 2009".
EPA-420-B-09-008. United States Environmental Protection Agency,
2009.
[6] M. Barth, F. An, T. Younglove, G. Scora, C. Levine, M. Ross, and T.
Wenzel, "Comprehensive Modal Emission Model (CMEM), version 2.0,
User-s Guide". University of California, Riverside, 2000.
[7] H. Kuhns, C. Mazzoleni, H. Moosmuller, D. Nikolc, R. Keislar, P. Barber,
L. Zheng, V. Etyemezian, and J.Watson, "Remote sensing of PM, NO, CO
and HC emission factors for on-road gasoline and diesel engine vehicles
in Las Vegas, NV". Science of the Total Environment, Vol. 322, No. 1-3,
pp. 123-137, 2004.
[8] N. Rogak, U. Pott, T. Dann, and D. Wang, "Gaseous emissions from
vehicles in a traffic tunnel in Vancouver, British Columbia". Air & Waste
Manage. Assoc, Vol. 48, No.7, pp.604-615, 1998.
[9] C. John, R. Friedrich, J. Staehelin, K. Schläpfer, and W.A. Stahel,
"Comparison of emission factors for road traffic from a tunnel study
(Gubrist tunnel, Switzerland) and from emission modeling". Atmospheric
Environment, Vol. 33, No. 20, pp. 3367-3376, 1999.
[10] F. Yang, and L.Yu, "A Microscopic Emission Model for the Light-Duty
Vehicles Based on PEMS Data". In Proceedings of International
Conference of Transportation Engineering 2007 (ICTE 2007), Chengdu,
China, 2007.
[11] D.A.M.M. Elst, R.T.M. Smokers, and J.W.De Koning, "Evaluation of the
Capabilities of On-Board Emission Measurement System for the Purpose
of Generating Real-Life Emission Factors". TNO Report
04.OR.VM.039.1/DE, 2004.
[12] G. Song, , L. Yu, and X. Zhang, "Emission Analysis at Toll Station Area
in Beijing with Portable Emission Measurement System". Transportation
Research Record: Journal of the Transportation Research Board, 2058,
pp.106-114, 2008.
[13] X. Li, G. Li, S. Pang, X. Yang, and J. Tian, "Signal timing of intersections
using integrated optimization of traffic quality, emissions and fuel
consumption: a note". Transportation Research Part D 9, Vol. 9, pp.
401-407, 2004.
[14] H. Liu, M. Barth, G. Scora, N. Davis, and J. Lents, "Using Portable
Emission Measurement System for Transportation Emissions Studies:
Comparison with Laboratory Methods". In Proceedings of 89th
Transportation Research Board Annual Meeting. USA, Washingtong
D.C., 2010.
[15] H. Rakha, K. Ahn, and A. Trani, "Development of VT-Micro model for
estimating hot stabilized light duty vehicle and truck emission".
Transportation Research Part D, Vol.9, pp.49-74, 2004.
[16] H. Teng, L. Yu, and Y. Qi, "Statistical micro-scale emission models
incorporating acceleration and deceleration". In Proceedings of 81th
Transportation Research Board Annual Meeting. USA , Washington D.C.,
2002.
[17] HORIBA, "On Board Emission Measurement System OBS-2200
Instruction Manual". HORIBA Ltd, 2005.
[18] Y. Kamarianakis, and H.O. Gao, "Accounting for exhaust gas transport
dynamics in instantaneous emission models via Smooth Transition
Regression". In Proceedings of 89th Transportation Research Board
Annual Meeting. USA Washington D.C., 2010.
[19] R.K. Jr, "Data smoothing using a least squares fit C++ class". ISA
Transactions, 37, pp.3-19, 1998.
[20] M. Barth, F. An, J. Norbeck, and M. Ross, "Modal Emissions Modeling:
A Physical Approach". Transportation Research Record: Journal of the
Transportation Research Board, 1520, pp.81-88, 1996.
[21] H. Guo, J. Zeng, and Y. Hu, "Neural Network Modeling of Vehicle Gross
Emitter Prediction Based on Remote Sensing Data". In Proceedings of the
2006 IEEE International Conference. Taipei, Taiwan, 2006
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:64882", author = "Wei Lei and Hui Chen and Lin Lu", title = "Microscopic Emission and Fuel Consumption Modeling for Light-duty Vehicles Using Portable Emission Measurement System Data", abstract = "Microscopic emission and fuel consumption models
have been widely recognized as an effective method to quantify real
traffic emission and energy consumption when they are applied with
microscopic traffic simulation models. This paper presents a
framework for developing the Microscopic Emission (HC, CO, NOx,
and CO2) and Fuel consumption (MEF) models for light-duty
vehicles. The variable of composite acceleration is introduced into
the MEF model with the purpose of capturing the effects of historical
accelerations interacting with current speed on emission and fuel
consumption. The MEF model is calibrated by multivariate
least-squares method for two types of light-duty vehicle using
on-board data collected in Beijing, China by a Portable Emission
Measurement System (PEMS). The instantaneous validation results
shows the MEF model performs better with lower Mean Absolute
Percentage Error (MAPE) compared to other two models. Moreover,
the aggregate validation results tells the MEF model produces
reasonable estimations compared to actual measurements with
prediction errors within 12%, 10%, 19%, and 9% for HC, CO, NOx
emissions and fuel consumption, respectively.", keywords = "Emission, Fuel consumption, Light-duty vehicle,Microscopic, Modeling.", volume = "4", number = "6", pages = "530-8", }