混合交通流环境下车辆编队与碳排放特性

doi:10.13229/j.cnki.jdxbgxb.20240775

Abstract

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

CLC Number:

U491

Wen-hui ZHANG,Mei-ru YE,Cong XI,Zi-wen SONG. Vehicle formation and carbon emission characteristics in mixed traffic flow environment[J].Journal of Jilin University(Engineering and Technology Edition), 2026, 56(2): 416-430.

Figures/Tables 16

Fig.1

Table 1

Parameter value in simulation"

参数	符号	数值
车身长度/m	$l n + 1$	5
最大速度/（m·s^-1）	$v m a x$	33
队内车间距/m	$d C A C C$	3
最大车队规模/辆	$Z$	6
期望加速度/（m·s^-2）	$a$	3
制动减速度/（m·s^-2）	$b$	5
HDV反应时间/s	$τ H D V$	1.4
CAV反应时间/s	$τ C A V$	0.5

Table 1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Table 2

Correlation coefficient Ki,je(K'i,je) of CO2 emission"

$K i, j e (K' i, j e)$		$j = 0$	$j = 1$	$j = 2$	$j = 3$
$a ≥ 0$	$i = 0$	6.916	0.217	2.345×10⁴	-3.639×10⁴
	$i = 1$	0.027 54	0.968×10²	-0.175×10²	8.35×10⁵
	$i = 2$	-2.070×10⁴	-1.013 8×10⁴	1.966×10⁵	-1.02×10⁶
	$i = 3$	9.80×10⁷	3.66×10⁷	-1.08×10⁷	8.50×10⁹
$a < 0$	$i = 0$	6.915	-0.032	-9.17×10³	-2.886×10⁴
	$i = 1$	0.028 4	8.53×10³	1.15×10³	-3.06×10⁶
	$i = 2$	-2.266×10⁴	-6.594×10⁵	-1.289×10⁵	-2.68×10⁷
	$i = 3$	1.11×10⁶	3.20×10⁷	7.56×10⁸	2.95×10⁹

Table 2

Fig.7

Table 3

Fig.8

Table 4

Table 5

Fig.9

Fig.10

Fig.11

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CAV 渗透率	CO₂排放总量/（L·km^-1）		下降比例/%
CAV 渗透率	离散模式	编队模式	下降比例/%
0.2	0.360 2	0.329 8	8.4
0.4	0.324 5	0.285 6	12.0
0.6	0.267 6	0.218 8	18.2
0.8	0.190 3	0.142 5	25.1

密度/ （辆·km^-1）	CO₂排放降低百分比/%
密度/ （辆·km^-1）	0.1	0.2	0.3	0.4	0.5	0.6
10	0.02	0.03	0.07	0.07	0.23	0.43
20	0.05	0.15	0.48	0.34	0.67	0.81
30	-1.40	-3.66	-2.54	-1.53	-2.82	-4.94
40	-0.20	0.10	-0.58	-0.41	-0.70	-1.83
50	0.02	0.37	0.50	0.42	0.32	-0.43
60	0.05	-0.01	0.10	-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

密度/ （辆·km^-1）	CO₂排放降低百分比/%
密度/ （辆·km^-1）	0.1	0.2	0.3	0.4	0.5	0.6
10	0.02	0.07	-0.07	0.01	0.01	0.03
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
70	0.05	-0.15	0.09	0.28	0.51	0.76
80	0.62	0.06	0.84	1.34	1.67	1.45

Vehicle formation and carbon emission characteristics in mixed traffic flow environment

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