Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (1): 150-158.doi: 10.13229/j.cnki.jdxbgxb20210601

Previous Articles    

Impacts of CO2 emissions and improving method for connected and automated mixed traffic flow

Yan-yan QIN1(),Xiao-qing YANG2,Hao WANG3()   

  1. 1.School of Traffic and Transportation，Chongqing Jiaotong University，Chongqing 400074，China
    2.Kunming Urban Planning & Design Institute Co. ，Ltd. ，Kunming 650041，China
    3.School of Transportation，Southeast University，Nanjing 210096，China
  • Received:2021-07-03 Online:2023-01-01 Published:2023-07-23
  • Contact: Hao WANG E-mail:qinyanyan@cqjtu.edu.cn;haowang@seu.edu.cn

RICH HTML

 0   

PDF (PC)

347

Abstract:

Mixed traffic flow will consist of cooperative adaptive cruise control （CACC） vehicles and manual driven （MD） vehicles under connected and automated environment. Then this paper focuses on CO2 emission of such mixed flow. Firstly， the mixed traffic flow in this paper was defined according to characteristics of connected and automated environment. Car-following behaviors of each type of vehicles in such mixed flow were described by car-following models calibrated via experimental data. Then numerical simulations were performed considering periodic boundary condition. Based on trajectory data in simulations， CO2 emission model was used to calculate impacts of CO2 emission for the mixed traffic. Finally， effect mechanism of CO2 emission was explored from the perspective of mixed flow stability. An improving method for reducing CO2 emission was also proposed. Results show that CO2 emission of such mixed flow would increase and then decrease， with the increase of CACC penetration rate p， which has qualitative relation with traffic flow stability. By using the proposed improving method based on variable headway strategy， CO2 emission of such mixed flow would decrease with the increase of p. Compared with MD vehicular flow （p=0）， CACC vehicular flow （p=1） could reduce CO2 emission by 19.35%. Results obtained in this paper can provide reference for management strategy of such mixed traffic flow， from the perspective of reducing CO2 emission.

Key words: transportation planning and management, mixed traffic flow, CO2 emissions, car-following model, cooperative adaptive cruise control

CLC Number: 

  • U491

Fig.1

Flow chart of CO2 emission analysis based on simulations"

Table 1

Comparative analysis of CO2 emission effect in mixed traffic flow"

CACC

渗透率p

CO2排放降低百分比/%
tc=0.6 s， ta=1.1 stc=0.6 s， ta=1.6 stc=0.6 s， ta=2.2 stc=0.9 s， ta=1.1 stc=1.1 s， ta=1.1 s
0.0/////
0.113.1612.57-1.0412.2912.15
0.217.068.23-2.0316.3716.01
0.310.83-1.90-3.4410.4410.06
0.4-1.56-3.23-4.65-2.22-2.52
0.5-4.09-5.90-7.98-4.91-5.78
0.6-6.56-8.06-10.17-7.22-7.53
0.7-7.91-9.61-12.25-7.98-8.14
0.8-10.23-12.58-14.2-10.35-10.37
0.9-13.89-14.92-16.11-14.96-15.79
1.0-19.35-19.35-19.35-20.60-21.33

Fig.2

Effect of taon CO2 emission of mixed traffic flow （tc=0.6 s）"

Fig.3

Effect of tcon CO2 emission of mixed traffic flow （ta=1.1 s）"

Fig.4

Effect of ta on stability of mixed traffic flow （tc=0.6 s）"

Fig.5

Effect of tc on stability of mixed traffic flow （ta=1.1 s）"

Table 2

Analytical relation between taand p*"

ta/sp*/%ta/sp*/%
1.134.521.78.19
1.231.741.80.12
1.328.491.9-
1.424.672.0-
1.520.152.1-
1.614.742.2-

Fig.6

Effect trend of CO2 emission after improvement"

1 张健, 吴坤润, 杨敏, 等. 智能网联环境下交叉口双环自适应控制模型[J]. 吉林大学学报:工学版, 2021, 51(2): 541-548.
Zhang Jian, Wu Kun-run, Yang Min, et al. Double-ring adaptive control model of intersection during intelligent and connected environment[J]. Journal of Jilin University (Engineering and Technology Edition), 2021, 51(2): 541-548.
2 Qin Y, Wang H, Ni D. Lighthill-whitham-richards model for traffic flow mixed with cooperative adaptive cruise control vehicles[J]. Transportation Science, 2021, 55(4): 883-907.
3 Li Y, Li Z, Wang H, et al. Evaluating the safety impact of adaptive cruise control in traffic oscillations on freeways[J]. Accident Analysis & Prevention, 2017, 104: 137-145.
4 Wang M. Infrastructure assisted adaptive driving to stabilise heterogeneous vehicle strings[J]. Transportation Research Part C: Emerging Technologies, 2018, 91: 276-295.
5 秦严严, 张健, 陈凌志, 等. 手动-自动驾驶混合交通流元胞传输模型[J]. 交通运输工程学报, 2020, 20(2): 229-238.
Qin Yan-yan, Zhang Jian, Chen Ling-zhi, et al. Cell transportation model of mixed traffic flow of manual-automated driving[J]. Journal of Traffic and Transportation Engineering, 2020, 20(2): 229-238.
6 Rahman M S, Abdel-Aty M, Wang L, et al. Understanding the highway safety benefits of different approaches of connected vehicles in reduced visibility conditions[J]. Transportation Research Record, 2018, 2672(19): 91-101.
7 Milanés V, Shladover S E. Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data[J]. Transportation Research Part C: Emerging Technologies, 2014, 48: 285-300.
8 Gong S, Zhou A, Peeta S. Cooperative adaptive cruise control for a platoon of connected and autonomous vehicles considering dynamic information flow topology[J]. Transportation Research Record, 2019, 2673(10): 185-198.
9 Zhou Y, Wang M, Ahn S. Distributed model predictive control approach for cooperative car-following with guaranteed local and string stability[J]. Transportation Research Part B: Methodological, 2019, 128: 69-86.
10 van Nunen E, Reinders J, Semsar-Kazerooni E, et al. String stable model predictive cooperative adaptive cruise control for heterogeneous platoons[J]. IEEE Transactions on Intelligent Vehicles, 2019, 4(2): 186-196.
11 秦严严, 王昊, 王炜, 等. 混有CACC车辆和ACC车辆的异质交通流基本图模型[J]. 中国公路学报, 2017, 30(10): 127-136.
Qin Yan-yan, Wang Hao, Wang Wei, et al. Fundamental diagram model of heterogeneous traffic flow mixed with cooperative adaptive cruise control vehicles and adaptive cruise control vehicles[J]. China Journal of Highway and Transport, 2017, 30(10): 127⁃136.
12 常鑫, 李海舰, 荣建, 等. 混有智能网联车队的交通流基本图模型分析[J]. 东南大学学报:自然科学版, 2020, 50(4): 782-788.
Chang Xin, Li Hai-jian, Rong Jian, et al. Analysis on fundamental diagram model for mixed traffic flow with connected vehicle platoons[J]. Journal of Southeast University (Natural Science Edition), 2020, 50(4): 782-788.
13 贾彦峰, 曲大义, 林璐, 等. 基于运行轨迹的网联混合车流速度协调控制[J].吉林大学学报:工学版, 2021, 51(6):2051-2060.
Jia Yan-feng, Qu Da-yi, Lin Lu, et al. Coordinated speed control of connected mixed traffic flow based on trajectory[J]. Journal of Jilin University (Engineering and Technology Edition), 2021, 51(6):2051-2060.
14 Jiang R, Wu Q, Zhu Z. Full velocity difference model for a car-following theory[J]. Physical Review E, 2001, 64:No. 017101.
15 王雪松, 孙平, 张晓春, 等. 基于自然驾驶数据的高速公路跟驰模型参数标定[J]. 中国公路学报, 2020, 33(5): 132-142.
Wang Xue-song, Sun Ping, Zhang Xiao-chun, et al. Calibrating car-following models on freeway based on naturalistic driving data [J]. China Journal of Highway and Transport, 2020, 33(5): 132-142.
16 Yu S, Shi Z. The effects of vehicular gap changes with memory on traffic flow in cooperative adaptive cruise control strategy[J]. Physica A: Statistical Mechanics and its Applications, 2015, 428: 206-223.
17 Zeng W, Miwa T, Morikawa T. Prediction of vehicle CO2 emission and its application to eco-routing navigation[J]. Transportation Research Part C: Emerging Technologies, 2016, 68: 194-214.
18 Ward J A. Heterogeneity, lane-changing and instability in traffic: a mathematical approach[D]. Bristol: University of Bristol, 2009.
[1] Dian-hai WANG,You-wei HU,Zheng-yi CAI,Jia-qi ZENG,Wen-bin YAO. Dynamic road resistance model of intermittent flow on urban roads based on BPR function [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(7): 1951-1961.
[2] Yan-bo LI,Bai-song LIU,Bo-bin YAO,Jun-shuo CHEN,Kai-fa QU,Qi-sheng WU,Jie-ning CAO. Location of electrical changing station of expressway considering stochastic characteristics of road network [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(5): 1364-1371.
[3] Ying HU,Chun-fu SHAO,Shu-ling WANG,Xi JIANG,Hai-rui SUN. Identification of road riding quality based on shared bike trajectory data [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(4): 1040-1046.
[4] Rui-fa LUO,Hui-jun HAO,Tao-rang XU,Qiu-fan GU. Fundamental diagram model of mixed traffic flow of connected and automated vehicles considering vehicles degradations and platooning intensity [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 405-412.
[5] Zhan-zhong WANG,Ting JIANG,Jing-hai ZHANG. Evaluation of road transportation efficiency based on fuzzy double frontiers network model [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 385-395.
[6] Guang-ming NIE,Bo XIE,Yan-tao TIAN. Design of cooperative adaptive cruise control algorithm based on Frenet framework [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(7): 1687-1695.
[7] Da-yi QU,Zi-xu ZHAO,Yan-feng JIA,Tao WANG,Qiong-hui LIU. Car⁃following dynamics characteristics and model based on Lennard⁃Jones potential [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(11): 2549-2557.
[8] Yun-juan YAN,Wei-xiong ZHA,Jun-gang SHI,Jian LI. Mixed network equilibrium model with stochastic charging demand [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(1): 136-143.
[9] Yan-feng JIA,Da-yi QU,Lu LIN,Rong-han YAO,Xiao-long MA. Coordinated speed control of connected mixed traffic flow based on trajectory [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 2051-2060.
[10] Hao LI,Hao CHEN. Mixed traffic network equilibrium with battery electric vehicles considering charging queuing time [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1684-1691.
[11] Zuo-an HU,Yi-ming XIA,Jia CAI,Feng XUE. Optimization of urban rail transit operation adjustment based on multiple strategies under delay [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1664-1672.
[12] Cai-hua ZHU,Xiao-li SUN,Yan LI. Forecast of urban public bicycle traffic demand by station classification [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 531-540.
[13] Qing-yu LUO,Wan-li TIAN,Hong-fei JIA. Location and capacity model of electric vehicle charging station considering commuting demand [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(5): 1471-1477.
[14] CAO Qian, LI Jun, LIU Yu, QU Da-wei. Construction of driving cycle based on Markov chain for passenger car in Changchun City [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1366-1373.
[15] SUN Bao-feng, GAO Kun, SHEN Xiu-xiu, LIANG Ting. Location model of gas station for network expansion based on capacity balance and variable coverage radius [J]. 吉林大学学报(工学版), 2018, 48(3): 704-711.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd