基于元胞自动机的绿色驾驶行为特征仿真

doi:10.3976/j.issn.1002-4026.2019.02.010

Abstract

Abstract: Eco-driving is a series of driving measures that can achieve the effect of fuel-efficiency improvement and emission reduction during the driving process. To study the behavioral characteristics of eco-driving, a multi-lane cellular automata model was established to simulate, and the data of a section of freeway in Beijing North Third Ring Road was used as a simulation scenario to calibrate and validate the model. Then, based on this computer simulation model, the effects of car-following driving behavior and lateral driving behavior on traffic operation, energy consumption and exhaust emission were revealed. The eco-driving behavior criterion was established and then, the eco-driving behavior characteristics were predicted. It is found that slow acceleration, maintaining the stability of the vehicle speed, keeping the vehicle speed at a reasonable interval and maintaining a large safety distance are the characteristics of eco-driving behaviors in line with Chinese road characteristics.

Key words: traffic engineering, eco-driving behavior, cellular automata, vehicle specific power, exhaust emission

CLC Number:

U471.15

LI Xing-dong, YUAN Zhen-zhou, ZHANG Yu , GUO Hong-wei. Characteristics simulation of eco-driving behavior based on cellular automata[J].Shandong Science, 2019, 32(2): 70-78.

References 17

[1]	SULLMAN M J M, DORN L, NIEMI P. Eco-driving training of professional bus drivers-Does it work?[J]. Transportation Research Part C: Emerging Technologies, 2015, 58(D): 749-759. doi: 10.1016/j.trc.2015.04.010
[2]	KAMAL M A S, MUKAI M, MURATA J, et al. On board eco-driving system for varying road-traffic environments using model predictive control[C]//2010 IEEE International Conference on Control Applications. Yokohama，JPN： IEEE, 2010: 1636-1641. http://dx.doi.org/10.1109/CCA.2010.5611196 doi: 10.1109/CCA.2010.5611196
[3]	QIAN G B, CHUNG E. Evaluating effects of eco-driving at traffic intersections based on traffic micro-simulation[EB/OL].［2018-05-18］.http://www.patrec.org/atrf.aspx.
[4]	PAMPEL S M, JAMSON S L, HIBBERD D L, et al. How I reduce fuel consumption: An experimental study on mental models of eco-driving[J]. Transportation Research Part C: Emerging Technologies, 2015, 58(D): 669-680. DOI: 10.1016/j.trc.2015.02.005 doi: 10.1016/j.trc.2015.02.005
[5]	HEDGES P, MOSS D. Costing the effectiveness of training: case study 1-improving Parcelforce driver performance[J]. Industrial and Commercial Training, 1996, 28(3): 14-18. doi: 10.1108/00197859610115430
[6]	HAWORTH N, SYMMONS M. Driving to reduce fuel consumption and improve road safety[EB/OL]. [2018-05-20]. http://acrs.org.au/files/arsrpe/RS010036.pdf
[7]	MATSUMOTO S, PARK T, KAWASHIMA H. A Comparative Study on Fuel Consumption Reduction Effects of Eco-Driving Instructions Strategies[J]. International Journal of Intelligent Transportation Systems Research, 2014, 12(1): 1-8. doi: 10.1007/s13177-013-0066-8
[8]	LARSSON H, ERICSSON E. The effects of an acceleration advisory tool in vehicles for reduced fuel consumption and emissions[J]. Transportation Research Part D: Transport and Environment, 2009, 14(2): 141-146. doi: 10.1016/j.trd.2008.11.004
[9]	KOBAYASHI I, TSUBOTA Y, KAWASHIMA H. Eco-driving simulation: evaluation of eco-driving within a network using traffic simulation[J]. WIT Transactions on The Built Environment, 2007, 96: 741-750. doi: 10.2495/UT070701
[10]	郑英力, 翟润平, 马社强. 交通流元胞自动机模型综述 [J]. 公路交通科技, 2006, 23(1): 110-115. doi: 10.3969/j.issn.1002-0268.2006.01.028
[11]	张笑梅. 基于元胞自动机和模糊推理的交通流模型研究 [D]. 大连：大连理工大学, 2013.
[12]	HABEL L, SCHRECKENBERG M. Asymmetric lane change rules for a microscopic highway traffic model[EB/OL].[2018-05-20]http://dx.doi.org/10.1007/978-3-319-11520-7_66. doi: 10.1007/978-3-319-11520-7_66
[13]	LI Q L, WONG S C, Min J, et al. A cellular automata traffic flow model considering the heterogeneity of acceleration and delay probability[J]. Physica A: Statistical Mechanics and its Applications, 2016, 456: 128-134. doi: 10.1016/j.physa.2016.03.026
[14]	SONG G, YU L. Estimation of fuel efficiency of road traffic by characterization of vehicle-specific power and speed based on floating car data[J]. Transportation Research Record: Journal of the Transportation Research Board, 2009,2139 (2139): 11-20. doi: 10.3141/2139-02
[15]	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. DOI： 10.1061/(ASCE)0733-947X(2002)128:2(182) doi: 10.1061/(ASCE)0733-947X(2002)128:2(182)
[16]	夏韡. 城市道路轻型车微观排放模型研究 [D]. 长春: 吉林大学, 2005.
[17]	DHAOU I B. Fuel estimation model for ECO-driving and ECO-routing[EB/OL]. [2018-05-20]http://dx.doi.org/10.1109/IVS.2011.5940399.

Characteristics simulation of eco-driving behavior based on cellular automata

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

References 17

Related Articles 11

Metrics

Comments

Recommended 0

[1]	HOU Mingye, WANG Xiaoyang, XU Qingjie, YANG Bo, WANG Xiaofeng. Long-term performance maintenance decisions for asphalt pavements based on reinforcement learning [J]. Shandong Science, 2023, 36(3): 108-114.
[2]	RUI Ying-xu,TANG Tie-qiao. Extended cellular automata model for bicycle flow considering group and retrograde behaviors [J]. Shandong Science, 2022, 35(2): 79-88.
[3]	WANG Jian, XU Zhi-hong , TONG Wei-ping , SONG Yu-jia , LIU Zhi-yuan . Optimization of coordinated signal settings for hook-turn intersections [J]. Shandong Science, 2020, 33(3): 109-118.
[4]	JIAO Xin-ping, WANG Jiang-feng, CHEN Lei, GAO Zhi-jun, DONG Jia-kuan, HUANG Hai-tao, YE Jing-song. Short-term traffic flow prediction algorithm based on HALRTC theory [J]. Shandong Science, 2019, 32(6): 62-68.
[5]	WANG Wei, WU Zhong. Study on the traffic flow model of urban tunnel section under signal control [J]. Shandong Science, 2019, 32(3): 57-64.
[6]	CHENG Jian-hui, LI De-wei. Mechanism study on the interference between pedestrians and vehiclesat unsignalized mid-block crosswalks based on cellular automata [J]. Shandong Science, 2019, 32(1): 83-92.
[7]	LAN Hui-hui, WU Xian-yu, YIN Yong-hao. Research on the method for estimating intersection turning volume based on the equilibrium of the link flow volumes [J]. SHANDONG SCIENCE, 2018, 31(3): 76-84.
[8]	CHEN Lun, GU Yuan-li，LU Wen-qi. Study on traffic state identification of urban expressway based on fuzzy comprehensive evaluation [J]. SHANDONG SCIENCE, 2017, 30(5): 62-69.
[9]	ZHUANG Guang-xin,GU Yuan-li, MA Yun-nan, ZHAO Ying. Road traffic flow data quality assessment based on grey theory and analysis hierarchical process [J]. SHANDONG SCIENCE, 2017, 30(1): 69-75.
[10]	CHEN Liang,TANG Tieqiao,SHAO Yixiao. Impact of chair and desk structures in a classroom on evacuation efficiency [J]. SHANDONG SCIENCE, 2015, 28(6): 87-92.
[11]	XIA Ping-Ping, WEI Li-Ying, MA Zhuang-Lin. Negative binomial regression model based analysis for influential factors of expressway traffic accidents [J]. J4, 2013, 26(4): 79-84.