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

山东科学 ›› 2019, Vol. 32 ›› Issue (2): 70-78.doi: 10.3976/j.issn.1002-4026.2019.02.010

基于元胞自动机的绿色驾驶行为特征仿真

李兴东¹，袁振洲¹，张羽²，郭宏伟³

1.北京交通大学交通运输学院，北京 100044；2.同济大学交通运输工程学院，上海 201804； 3.北京理工大学机械与车辆学院，北京 100081

收稿日期:2018-06-03 出版日期:2019-04-20 发布日期:2019-04-02
作者简介:李兴东（1993－），男，硕士研究生，研究方向为绿色驾驶行为模式、交通规划。E-mail:15910485390@163.com
基金资助:
国家自然科学基金（71301010，51238008)

Characteristics simulation of eco-driving behavior based on cellular automata

LI Xing-dong¹,YUAN Zhen-zhou¹，ZHANG Yu² ，GUO Hong-wei³

1. Beijing Jiaotong University，School of Transportation，Beijing 100044, China; 2. School of Transportation Engineering, Tongji University, Shanghai 201804, China;3. School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China

Received:2018-06-03 Online:2019-04-20 Published:2019-04-02

摘要/Abstract

摘要： 绿色驾驶是可以使驾驶人在驾驶过程中节油减排的一系列驾驶措施。为了研究绿色驾驶的行为特征，建立了多车道元胞自动机模型进行仿真，并以北京北三环某路段作为仿真场景对模型进行标定和验证。利用该仿真模型，研究纵向驾驶行为和横向驾驶行为对交通运行、能源消耗以及尾气排放产生的影响。构建了绿色驾驶行为判别标准，并通过预测得到绿色驾驶行为特征。研究发现，缓慢加速、保持车速稳定、将车速保持在合理区间以及保持较大的安全间距是符合中国道路特性的绿色驾驶行为。

关键词: 交通工程, 绿色驾驶行为, 元胞自动机, 比功率, 尾气排放

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

中图分类号:

U471.15

李兴东, 袁振洲, 张羽, 郭宏伟. 基于元胞自动机的绿色驾驶行为特征仿真[J]. 山东科学, 2019, 32(2): 70-78.

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.

参考文献 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.

基于元胞自动机的绿色驾驶行为特征仿真

Characteristics simulation of eco-driving behavior based on cellular automata

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