GA-based approach to modeling operating mode distributions and estimating emissions
XU Yao-fang1, YU Lei2,3, SONG Guo-hua3
1. School of Traffic and Transportation, Chongqing Jiaotong University 400074, China;
2. Texas Southern University, Texas 77004, USA;
3. Beijing Jiaotong University, Beijing 100044, China
In view of the widely available data of the average speed in traffic systems and the parameter of operating mode distribution needed in emission models, a model was developed for generating operating mode distribution based on the average speed. Genetic Algorithm was used further for optimizing the model. After a comparison with the real-world data, it was found that the average emission rates estimated based on the proposed model exhibited less errors than those estimated based on MOVES in 82.5% of the speed bins. The highest error based on the proposed model was 50% while 304.2% based on MOVES. Finally, the proposed model was used to estimate emissions in Beijing for pre- and after- the implementation of the vehicle restriction policy. It was found that the 2nd Ring Road has experienced a reduction of total emissions of HC, CO, NOx by 9.58%, 11.41%, 0.49% respectively. Compare with the test values, the R2 with the model were higher than 0.700 and errors of the model were much lower than that of the MOVES. Further a modal application was proposed, which can calculate the traffic exhaust emissions of dynamic network.
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U.S.Environmental Protection Agency (EPA).Motor vehicle emission simulator highway vehicle implementation (MOVES-HVI) demonstration version:Software design and reference manual draft.publication EPA420-P-07-001[R].Washington D.C.,Office of Transportation and Air Quality,U.S.Environmental Protection Agency,2007.
[2]
Barth M,An F,Younglove T,et al.Development of a comprehensive modal emission Model-Final report[R]Washington D.C.,Transportation Research Board National Research Council.April 2000.
[3]
Ahn K,Rakha H,Trani A,et al.Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels[J].Journal of Transportation Engineering.2002,128(2):182-190.
[4]
Davis,N,James L,Mauricio O,et al.Development and application of an international vehicle emissions Model[J].In Transportation Research Record:Journal of the Transportation Research Board,2005,1939:157-165.
Zhang Y Y,Chen X,Zhang X,et al.Assessing Effect of traffic signal control strategies on vehicle emissions journal of transportation systems engineering and information technology,2009,9(1):150-155.
[11]
杨方.基于PEMS的轻型车热稳状态下微观尾气排放模型[D].北京:北京交通大学,2005.
[12]
Liu H,He K B,Lents J.A methodology for developing a micro-emission model based on on-board heavy duty truck tests in China[C]//87th Transportation Research Board Annual Meeting CD-ROM,Washington,D.C.,USA,2008.
[13]
陈琨.微观交通仿真与尾气评估[D].北京:北京交通大学,2003.
[14]
Lents J,Walsh M,K.He,et al.Handbook of air quality management[EB/OL].http://www.aqbook.org/read/?page=86,2009.
[15]
Frey H C,Rouphail N M,and Zhai H.Speed-and facility-specific emission estimates for on-road light-duty vehicles on the basis of real-world speed profiles[J].Transportation Research Record:Journal of the Transportation Research Board,2006,1987:128-137.
Jose L J Understanding and quantifying motor vehicle emissions with vehicle specific power and TILDAS remote sensing[D].Cambridge US:Massachusetts Institute of Technology,1999.
[18]
Jong K A D.An analysis of the behavior of a class of genetic adaptive systems[D],U.S:University of Michigan,1975.
[19]
Goldberg,D E.Genetic algorithms in search,optimization,and machine learning[M].U.S:Addison-Wesley Publishing.Co.Inc.,1989.
