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清华大学学报(自然科学版)  2023, Vol. 63 Issue (11): 1760-1769    DOI: 10.16511/j.cnki.qhdxxb.2023.26.029
  低碳交通与绿色发展 本期目录 | 过刊浏览 | 高级检索 |
臧金蕊1, 焦朋朋1, 宋国华2, 王天实3, 王健宇1
1. 北京建筑大学 土木与交通工程学院, 通用航空技术北京实验室, 北京 100044;
2. 北京交通大学 交通运输学院, 综合交通运输大数据应用技术交通运输行业重点实验室, 北京 100044;
3. 清华大学 土木工程系, 北京 100084
Eco-driving evaluation and trajectory optimization based on vehicle specific power distribution
ZANG Jinrui1, JIAO Pengpeng1, SONG Guohua2, WANG Tianshi3, WANG Jianyu1
1. Beijing Key Laboratory of General Aviation Technology, School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China;
2. Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China;
3. Department of Civil Engineering, Tsinghua University, Beijing 100084, China
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摘要 生态驾驶是节能减排的重要手段。为对驾驶行为的生态性水平进行定量评价及优化,该文基于大量驾驶员逐秒轨迹数据,构建了大量驾驶员整体与驾驶员个体的机动车比功率(vehicle specific power,VSP)分布模型,并基于整体与个体VSP分布差异提出驾驶行为生态性定量评价模型,识别实际生态驾驶轨迹,构建了基于人类实际驾驶特征的生态驾驶轨迹优化方法。结果表明:整体与个体VSP分布差异可量化评估油耗水平,实现对驾驶行为生态性的定量评价。该文构建的正弦函数多项式生态轨迹曲线,对不环保驾驶可达到7.63%的节油效果,可为驾驶员提供符合人类驾驶特征且易于遵循的生态轨迹优化曲线。
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关键词 生态驾驶驾驶行为轨迹优化机动车比功率    
Abstract:[Objective] Eco-driving is an important way of reducing emissions and conserving. However, in previous research, the evaluation and trajectory optimization techniques of eco-driving behavior have primarily been based on traffic simulation and driving simulator technologies, considering less the actual driving characteristics of human beings. In practice, eco-driving optimization curves are difficult for drivers to follow. The purpose of this study is to propose a novel quantitative evaluation method of eco-driving behavior based on the difference of vehicle specific power (VSP) distributions between a large number of drivers and an individual driver and to develop an eco-driving trajectory optimization model that conforms to human driving habits. [Methods] First, the baseline speed-specific VSP distributions are developed based on 754 000 records of second-by-second vehicle activity data of driving trajectories from 159 drivers on expressways in Beijing. The individual driver's speed-specific VSP distributions are developed for comparison to the baseline VSP distributions. Based on the discovered variations, a model is proposed to assess eco-driving behaviors based on the identified differences to quantify the ecological level of driving behaviors for various speed ranges. Then, based on the eco-driving assessment model suggested in this study, a significant number of real eco-driving trajectories are found. The back propagation (BP) neural network, polynomial, and sine function fitting techniques are used, and the fitting accuracy is assessed using the goodness of fit and root mean-square error. The optimum fitting approach is used to build the eco-driving trajectory fitting method. Finally, to prove the viability of the approach suggested in this case study, the non-environmental trajectories are optimized using the ecological trajectory optimization model as a case study. [Results] The results showed that: (1) The differences between the baseline and individual VSP distributions effectively evaluated ecological driving behavior, and the scoring method for ecological driving behavior was constructed to quantitatively evaluate the ecological degree of driving behavior ranging from 0 to 10. (2) The goodness of fit of the quintic polynomial of the sine function to the actual ecological driving trajectory was 0.999 8, which was the highest of the three fitting methods. The sine function polynomial fitted the acceleration and deceleration trends of the eco-driving trajectory well. (3) The eco-driving trajectory optimization method had a good fuel-saving effect, and the overall fuel consumption of non-environmental trajectories was reduced by 7.63% on average.(4) The case study showed that the fuel consumption of non-environmental trajectories was reduced, and the stability of non-environmental trajectories was improved after the optimization of ecological trajectory curves developed in this paper. By analyzing the differences between the baseline and individual VSP distributions, the ecological degree of the driving behavior could be quantitatively evaluated, and the actual eco-driving trajectory could be effectively identified. The eco-driving trajectory optimization model proposed in this paper had a good effect on reducing fuel consumption. [Conclusions] The conclusions in this research fill the gap left by the existing trajectory optimization models that neglect to consider human driving factors. In order to assist in reaching carbon peaking and carbon neutrality, this paper offers practical ecological driving recommendations that take into account the driving characteristics of human beings, are easy to implement, and help to achieve carbon peaking and carbon neutrality.
Key wordseco-driving    driving behavior    trajectory optimization    vehicle specific power
收稿日期: 2022-12-26      出版日期: 2023-10-16
通讯作者: 焦朋朋,教授,     E-mail:
臧金蕊, 焦朋朋, 宋国华, 王天实, 王健宇. 基于机动车比功率分布的生态驾驶评价与轨迹优化[J]. 清华大学学报(自然科学版), 2023, 63(11): 1760-1769.
ZANG Jinrui, JIAO Pengpeng, SONG Guohua, WANG Tianshi, WANG Jianyu. Eco-driving evaluation and trajectory optimization based on vehicle specific power distribution. Journal of Tsinghua University(Science and Technology), 2023, 63(11): 1760-1769.
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[1] YU Y, LI S Q, SUN H P, et al. Energy carbon emission reduction of China's transportation sector:An input-output approach[J]. Economic Analysis and Policy, 2021, 69:378-393.
[2] XIN Y L, SHAO S Q, WANG Z C, et al. COVID-2019 lockdown in Beijing:A rare opportunity to analyze the contribution rate of road traffic to air pollutants[J]. Sustainable Cities and Society, 2021, 75:102989.
[3] XU N, LI X H, LIU Q, et al. An overview of eco-driving theory, capability evaluation, and training applications[J]. Sensors, 2021, 21(19):6547.
[4] SANGUINETTI A, QUEEN E, YEE C, et al. Average impact and important features of onboard eco-driving feedback:A meta-analysis[J]. Transportation Research Part F:Traffic Psychology and Behaviour, 2020, 70:1-14.
[5] NG E C Y, HUANG Y H, HONG G, et al. Reducing vehicle fuel consumption and exhaust emissions from the application of a green-safety device under real driving[J]. Science of the Total Environment, 2021, 793:148602.
[6] XU Y R, ZHENG Y, YANG Y. On the movement simulations of electric vehicles:A behavioral model-based approach[J]. Applied Energy, 2021, 283:116356.
[7] ADAMIDIS F K, MANTOUKA E G, VLAHOGIANNI E I. Effects of controlling aggressive driving behavior on network-wide traffic flow and emissions[J]. International Journal of Transportation Science and Technology, 2020, 9(3):263-276.
[8] KUMMETHA V C, KONDYLI A. Simulator-based framework to incorporate driving heterogeneity via a biobehavioral extension to the intelligent driver model[J/OL]. Transportation Research Record:Journal of the Transportation Research Board.[2023-01-10]. DOI:10.1177/03611981221134630.
[9] LI G F, LAI W J, SUI X X, et al. Influence of traffic congestion on driver behavior in post-congestion driving[J]. Accident Analysis&Prevention, 2020, 141:105508.
[10] GAO J B, CHEN H B, LIU Y, et al. The effect of after-treatment techniques on the correlations between driving behaviours and NOx emissions of passenger cars[J]. Journal of Cleaner Production, 2021, 288:125647.
[11] HUANG Y H, NG E C Y, ZHOU J L, et al. Impact of drivers on real-driving fuel consumption and emissions performance[J]. Science of the Total Environment, 2021, 798:149297.
[12] ZHANG Z Y, SONG G H, CHEN J Y, et al. Development of a simplified model of speed-specific vehicle-specific power distribution based on vehicle weight for fuel consumption estimates[J]. Transportation Research Record:Journal of the Transportation Research Board, 2020, 2674(12):52-67.
[13] FAFOUTELLIS P, MANTOUKA E G, VLAHOGIANNI E I. Eco-driving and its impacts on fuel efficiency:An overview of technologies and data-driven methods[J]. Sustainability, 2020, 3(1):226.
[14] ZHANG Z Y, SONG G H, ZHANG L Q, et al. How do errors occur when developing speed correction factors for emission modeling?[J]. Transportation Research Part D:Transport and Environment, 2021, 101:103094.
[15] HUANG J C, SONG G H, ZHANG Z Y, et al. High-resolution fuel consumption model for better characterizations of high-speed scenarios[J]. Transportation Research Record:Journal of the Transportation Research Board, 2023, 2677(1):110-128.
[16] YAO H D, LI X P. Lane-change-aware connected automated vehicle trajectory optimization at a signalized intersection with multi-lane roads[J]. Transportation Research Part C:Emerging Technologies, 2021, 129:103182.
[17] DONG H X, ZHUANG W C, CHEN B L, et al. Predictive energy-efficient driving strategy design of connected electric vehicle among multiple signalized intersections[J]. Transportation Research Part C:Emerging Technologies, 2022, 137:103595.
[18] HE W N, DUAN L, ZHANG Z Y, et al. Analysis of the characteristics of real-world emission factors and VSP distributions:A case study in Beijing[J]. Sustainability, 2022, 14(18):11512.
[19] LI Z F, SONG G H, YU X J, et al. Developing operating mode distributions from sparse trajectories for emission estimation[J]. Transportation Research Record:Journal of the Transportation Research Board, 2019, 2673(2):137-148.
[20] ZHAI Z Q, XU J S, SONG G H, et al. Comparative analysis of drive-cycles, speed limit violations, and emissions in two cities:Toronto and Beijing[J]. Science of The Total Environment, 2022, 811:152323.
[21] SONG G H, YU L, WANG Z Q L. Aggregate fuel consumption model of light-duty vehicles for evaluating effectiveness of traffic management strategies on fuels[J]. Journal of Transportation Engineering, 2009, 135(9):611-618.
[22] ZHANG Z Y, SONG G H, ZHAI Z Q, et al. How many trajectories are needed to develop facility-and speed-specific vehicle-specific power distributions for emission estimation?Case study in Beijing[J]. Transportation Research Record:Journal of the Transportation Research Board, 2019, 2673(11):779-790.
[23] SONG S H, XIONG X Y, WU X, et al. Modeling the SOFC by BP neural network algorithm[J]. International Journal of Hydrogen Energy, 2021, 46(38):20065-20077.
[24] LING X, LEONG Z Q, CHIN C, et al. Regression analysis and curve fitting for the hydrodynamic coefficients of an underwater vehicle undergoing straight-ahead motion[J]. Ocean Engineering, 2022, 262:112135.
[25] HOXHA I, MEKLACHI T. Data fitting with rational functions:Scaled null space method with applications of fitting large scale shocks on economic variables and s-parameters[J]. Algorithms, 2022, 15(2):57.
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