Microscopic simulation parameter calibration of CMEM model for temporary maintenance zone

GAO Tian-zhi; CHEN Kuan-min

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GAO Tian-zhi, CHEN Kuan-min. Microscopic simulation parameter calibration of CMEM model for temporary maintenance zone[J]. Journal of Traffic and Transportation Engineering, 2016, 16(6): 114-124.

Citation:

GAO Tian-zhi, CHEN Kuan-min. Microscopic simulation parameter calibration of CMEM model for temporary maintenance zone[J]. Journal of Traffic and Transportation Engineering, 2016, 16(6): 114-124.

GAO Tian-zhi, CHEN Kuan-min. Microscopic simulation parameter calibration of CMEM model for temporary maintenance zone[J]. Journal of Traffic and Transportation Engineering, 2016, 16(6): 114-124.

Citation:

GAO Tian-zhi, CHEN Kuan-min. Microscopic simulation parameter calibration of CMEM model for temporary maintenance zone[J]. Journal of Traffic and Transportation Engineering, 2016, 16(6): 114-124.

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Microscopic simulation parameter calibration of CMEM model for temporary maintenance zone

School of Highway, Chang'an University, Xi'an 710064, Shaanxi, China

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Author Bio:
GAO Tian-zhi(1979-), male, lecturer, doctoral student, +86-29-82334027, 50613564@qq.com

CHEN Kuan-min(1957-), male, professor, PhD, +86-29-82336690, chenkm@yeah.net
Received Date: 2016-06-29
Publish Date: 2016-12-25

Abstract

Abstract

In order to ensure the accuracy of automobile exhaust emission calculation, the parameters of microscopic simulation model for temporary maintenance zone were calibrated.The temporary maintenance zone located on the Xuwei Highway in Henan Province was taken as example, the traffic data were measured through field investigation.The VISSIM traffic simulation model was built.According to the measured data, the macroscopic parameters of traffic volume and traffic composition were calibrated, the microscopic parameters such as desired speed, desired acceleration were calibrated by using the values of feature points.Four parameters of car-following model including headway time, following variation, threshold for entering following status and vibration acceleration were calibrated by using orthogonal test.According to the valid simulation results, the numerical relationship between the desired speed and the running speed was determined.The emission of automobile exhaust was calculated by using the CMEM model with valid simulated data, and the calculation formulae of emissions were put forward based on average speeds.Analysis result shows the simulated speeds only calibrated by using macroscopic parameters are apparently different from the measured speeds, and the averagerelative errors of speeds for passenger car and truck are 11.36% and 35.12%.Combined with microscopic parameters calibration, the average relative error between measured and simulated speeds is lower than 3%.The desired speeds are 1.270 and 1.165 times of running speeds for passenger car and truck.After calibration, the relative error of simulated and measured emissions is less than 7%, and the calibration effect of model is remarkable.
- traffic engineering,
- temporary maintenance zone,
- automobile exhaust emission,
- CMEM,
- VISSIM,
- parameter calibration

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References(22)

References

[1]	SONG Guo-hua, YU Lei, ZHANG Yan-hong. Applicability of traffic microsimulation models in vehicle emissions estimates—case study of VISSIM[J]. Transportation Research Record, 2012(2270): 132-141.
[2]	ANYA A R, ROUPHAIL N M, FREY H C, et al. Application of AIMSUN microsimulation model to estimate emissions on signalized arterial corridors[J]. Transportation Research Record, 2014(2428): 75-86.
[3]	SONG Guo-hua, YU Lei, XU Long. Comparative analysis of car-following models for emissions estimation[J]. Transportation Research Record, 2013(2341): 12-22.
[4]	MAHBUB P, GOONETILLEKE A, AYOKO G A. Prediction model of the buildup of volatile organic compounds on urban roads[J]. Environmental Science and Technology, 2011, 45(10): 4453-4459. doi: 10.1021/es200307x
[5]	BARTH M, AN F, YOUNGLOVE T, et al. Comprehensive modal emissions model(CMEM)version 2.02: user's guide[R]. Riverside: University of California, 2001.
[6]	VITI F, HOOGENDOORN S P, VAN ZUYLEN H J, et al. Speed and acceleration distributions at a traffic signal analyzed from microscopic real and simulated data[C]//IEEE. Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems. New York: IEEE, 2008: 651-656.
[7]	HE Chun-yu, WANG Qi-dong. Vehicle emission factors determination using CMEM in Beijing[J]. Research of Environmental Sciences, 2006, 19(1): 109-112. (in Chinese). doi: 10.3321/j.issn:1001-6929.2006.01.027
[8]	XU Cheng-wei, WU Chao-zhong, CHU Xiu-min, et al. Light-duty vehicle average emission factors of Wuhan City based on CMEM model[J]. Computer and Communications, 2008, 26(4): 185-188. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JTJS200804049.htm
[9]	WANG Yi, HE Jie, LI Xu-hong, et al. Simulation analysis of vehicle emissions in Jiuhuashan Tunnel based on the software VISSIM[J]. Journal of Wuhan University of Technology, 2012, 34(1): 109-113. (in Chinese). doi: 10.3963/j.issn.1671-4431.2012.01.024
[10]	LIU Yong-hong, LIAO Han-bo, YU Zhi, et al. Study of intersection control mode evaluation based on environmental effect[J]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 2013, 52(1): 12-16. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-ZSDZ201301002.htm
[11]	BENEKOHAL R F. Procedure for validation of microscopic traffic flow simulation models[J]. Transportation Research Record, 1991(1320): 190-202.
[12]	HOURDAKIS J, MICHALOPOULOS P G, KOTTOMMANNIL J. A practical procedure for calibrating microscopic traffic simulation models[J]. Transportation Research Record, 2003(1852): 130-139.
[13]	CIUFFO B F, PUNZO V, TORRIERI V. A framework for the calibration of microscopic traffic flow models[C]//TRB. Transportation Research Board 86th Annual Meeting. Washington DC: TRB, 2007: 1-14.
[14]	LEE H J, KOUTRAKIS P. Daily ambient NO2concentration predictions using satellite ozone monitoring instrument NO2data and land use regression[J]. Environmental Science and Technology, 2014, 48(4): 2305-2311.
[15]	WU J, BRACKSTONE M, MCDONALD M. The validation of a microscopic simulation model: a methodological case study[J]. Transportation Research Part C: Emerging Technologies, 2003, 11(6): 463-479. doi: 10.1016/j.trc.2003.05.001
[16]	PARK B, QI H. Development and evaluation of a procedure for the calibration of simulation models[J]. Transportation Research Record, 2005(1934): 208-217.
[17]	KIM S J, KIM W, RILLETT L R. Calibration of microsimulation models using nonparametric statistical techniques[J]. Transportation Research Record, 2005(1935): 111-119.
[18]	ASAMER J, VAN ZUYLEN H J, HEILMANN B. Calibrating VISSIM to adverse weather conditions[C]//University of Leuven. 2nd International Conference on Models and Technologies for Intelligent Transportation Systems. Leuven: University of Leuven, 2011: 1-5.
[19]	SUN Jian, YANG Xiao-guang. Research into microscopic traffic simulation model systematic parameter calibration: a case study of VISSIM[J]. Computer and Communications, 2004, 22(3): 3-6. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JTJS200403000.htm
[20]	WILMINK I, VITI F, VAN BAALEN J, et al. Emission modelling at signalised intersections using microscopic models[C]//The Intelligent Transportation Society of America. Proceedings of16th ITS World Congress and Exhibition on Intelligent Transport Systems and Services. Washington DC: The Intelligent Transportation Society of America, 2009: 1-8.
[21]	ZHANG Yu, YU Lei, ZHAO Na-le, et al. Application of simultaneous perturbation stochastic approximation algorithm in parameter calibration of VISSIM microscope simulation model[J]. Journal of Transportation Systems Engineering and Information Technology, 2010, 10(4): 44-49. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201004008.htm
[22]	HU Xing-hua, ZHANG Yu. Large-scale network traffic simulation parameter calibration method based on SPGA algorithm[J]. Journal of Educational Institute of Jilin Province, 2013, 29(5): 149-150. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JLJB201305068.htm