吉林大学学报(工学版) ›› 2026, Vol. 56 ›› Issue (2): 416-430.doi: 10.13229/j.cnki.jdxbgxb.20240775

• 交通运输工程·土木工程 • 上一篇    

混合交通流环境下车辆编队与碳排放特性

张文会(),叶梅茹,席聪,宋子文   

  1. 东北林业大学 土木与交通学院,哈尔滨 150040
  • 收稿日期:2024-07-13 出版日期:2026-02-01 发布日期:2026-03-17
  • 作者简介:张文会(1978-),男,教授,博士. 研究方向:交通运输规划与管理. E-mail: rayear@163.com
  • 基金资助:
    国家自然科学基金项目(52572369)

Vehicle formation and carbon emission characteristics in mixed traffic flow environment

Wen-hui ZHANG(),Mei-ru YE,Cong XI,Zi-wen SONG   

  1. School of Civil Engineering and Transportation,Northeast Forestry University,Harbin 150040,China
  • Received:2024-07-13 Online:2026-02-01 Published:2026-03-17

摘要:

针对混合交通流中传统模型难以精准刻画车队动态演化与人工驾驶车辆(HDV)异质行为交互的问题,提出了一种融合离散运动规则与动态编队演化的混合交通流建模框架。首先,设计离散运动安全距离模型,通过元胞间距整数决策规则解决连续加速度建模的失真问题;其次,构建基于马尔可夫链的车队规模转移概率矩阵,动态表征车队分裂、合并与重组过程;最后,基于多场景仿真数据量化队内车间距、反应时间、换道行为及车队规模对CO?排放的影响机制。结果表明:当智能网联车辆(CAV)渗透率>0.6时,编队模式才能有效改善混合交通流的运行状态,并显著降低CO?排放,降幅达18.2%~25.1%;优化队内车间距可提升减排效率,减排峰值高达42.5%;换道策略需适配交通密度,低密度下提升HDV换道概率可实现碳减排,而高密度时CAV换道概率需控制在0.4~0.6;3~5辆编队规模为减排效率最优区间。本文揭示了队列动态参数对碳排放的作用机制,为智能网联车队协同控制策略优化及低碳交通系统设计提供了理论支撑。

关键词: 交通运输系统工程, 混合交通流, 车辆队列, 离散运动规则, 动态演化, 碳排放

Abstract:

To address the limitations of traditional models in accurately characterizing the dynamic evolution of vehicle platoons and interactions with heterogeneous behaviors of human-driven vehicles (HDV) in mixed traffic flows, this paper proposes a hybrid traffic flow modeling framework integrating discrete motion rules and dynamic platoon evolution. First, a discrete motion safety distance model is developed to resolve the distortion problem in continuous acceleration modeling by introducing integer decision rules based on cellular spacing. Second, a platoon size transition probability matrix is constructed using Markov chains to dynamically characterize the splitting, merging, and reorganization processes of platoons. Finally, multi-scenario simulation data are employed to quantify the impact mechanisms of intra-platoon spacing, reaction time, lane-changing behavior, and platoon size on CO? emissions. The results indicate that: When the CAV penetration rate exceeds 0.6, platoon mode can effectively improve the operational state of mixed traffic flow, and significantly reduces CO? emissions, with a reduction range of 18.2% to 25.1%; Optimizing intra-platoon spacing achieves a peak emission reduction of 42.5%; Lane-changing strategies must adapt to traffic density—enhancing HDV lane-changing probability under low density can achieve carbon emission reductions, while restricting CAV lane-changing probability to 0.4~0.6 under high density; Platoon sizes of 3~5 vehicles demonstrate optimal emission reduction efficiency. This paper reveals the mechanism of queue dynamic parameters on carbon emissions, and provides theoretical support for the optimization of intelligent connected fleet cooperative control strategies and the design of low-carbon transportation systems.

Key words: engineering of communication and transportation system, mixed traffic flow, vehicle platoon, discrete movement rules, dynamic evolution, carbon emissions

中图分类号: 

  • U491

图1

安全距离示意图"

表1

仿真参数值"

参数符号数值
车身长度/mln+15
最大速度/(m·s-1vmax33
队内车间距/mdCACC3
最大车队规模/辆Z6
期望加速度/(m·s-2a3
制动减速度/(m·s-2b5
HDV反应时间/sτHDV1.4
CAV反应时间/sτCAV0.5

图2

速度-密度基本图"

图3

流量-密度基本图"

图4

低密度状态不同CAV渗透率下的时空图"

图5

高密度状态不同CAV渗透率下的时空图"

图6

不同CAV渗透率下的速度波动图"

表2

CO2排放的相关系数Ki,je(K'i,je) (mL·s-1)"

Ki,je(K'i,je)j=0j=1j=2j=3
a0i=06.9160.2172.345×104-3.639×104
i=10.027 540.968×102-0.175×1028.35×105
i=2-2.070×104-1.013 8×1041.966×105-1.02×106
i=39.80×1073.66×107-1.08×1078.50×109
a<0i=06.915-0.032-9.17×103-2.886×104
i=10.028 48.53×1031.15×103-3.06×106
i=2-2.266×104-6.594×105-1.289×105-2.68×107
i=31.11×1063.20×1077.56×1082.95×109

图7

不同工况下的CO2排放拟合"

表3

离散模式和编队模式下的CO2排放"

CAV

渗透率

CO2排放总量/(L·km-1下降比例/%
离散模式编队模式
0.20.360 20.329 88.4
0.40.324 50.285 612.0
0.60.267 60.218 818.2
0.80.190 30.142 525.1

图8

不同队内车间距下的CO2排放"

表4

HDV换道概率对CO2排放影响对比分析"

密度/

(辆·km-1

CO2排放降低百分比/%
0.10.20.30.40.50.6
100.020.030.070.070.230.43
200.050.150.480.340.670.81
30-1.40-3.66-2.54-1.53-2.82-4.94
40-0.200.10-0.58-0.41-0.70-1.83
500.020.370.500.420.32-0.43
600.05-0.010.10-0.20-0.15-0.32
70-0.03-0.55-0.80-0.46-1.18-0.80
80-0.06-0.15-0.38-0.17-0.58-0.27

表5

CAV换道概率对CO2排放影响对比分析"

密度/

(辆·km-1

CO2排放降低百分比/%
0.10.20.30.40.50.6
100.020.07-0.070.010.010.03
20-4.24-1.34-2.36-3.07-11.82-1.76
30-4.15-5.30-4.99-5.07-7.66-4.54
40-5.67-2.79-1.81-4.79-4.90-2.25
50-2.29-1.57-2.50-3.15-3.95-3.46
60-2.38-1.98-2.35-2.71-2.33-2.32
700.05-0.150.090.280.510.76
800.620.060.841.341.671.45

图9

不同反应时间下的CO2排放"

图10

不同反应时间与队内车间距下的CO2排放"

图11

不同车队规模和CAV渗透率下的CO2排放"

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