局部多车影响下跟驰行为机理及建模

doi:10.13229/j.cnki.jdxbgxb.20230584

Abstract

Abstract:

Aiming at the limitations of single-dimensional car-following model to describe vehicle car-following behavior in local multi-vehicle environment， the mechanism of vehicles in adjacent lane influencing the subject vehicle car-following behavior is explored， and a vehicle car-following model more suitable for local multi-vehicle environment is attempted to be established. The driving behavior variable reflecting the influence of vehicles in adjacent lane was determined by correlation analysis. Vortisch indicator of similarity（VIS） was used to characterize the influence of vehicles in adjacent lane on car-following behavior. Chi-square independent test and kernel density curve were used to determine the VIS demarcation threshold reflecting whether the influence is significant. Recursive feature elimination was used to screen the variables related to car-following samples significantly affected by vehicles in adjacent lane. The influence mechanism of variables was determined according to the statistical analysis results. Based on the mechanism proposed， the car-following model suitable for local multi-vehicle environment was constructed and its prediction effect was evaluated. Results show that VIS between the speed of subject vehicle and following vehicle in adjacent lane can characterize the influence and the VIS threshold is 0.668. It is concluded that the attention mechanism and memory effect can explainthe influence mechanism of car-following behavior in multi-vehicle environment. Considering the attention mechanism and memoryeffect， RMSE decrease by 65% in full velocity difference model （FVD） model and 62% in intelligent driver model （IDM） model， MAE decrease by 65% and 59% and R² increased by 180% and 288% respectively， which proved the rationality of the attention mechanism and memory effect in explaining the car following behavior of subject vehicle in local multi-vehicle environment.

Key words: traffic and transportation safety engineering, multi-vehicle environment, car-following behavior, similarity indicator, recursive feature elimination, intention mechanism, memory effect

CLC Number:

U491.2

Lan-fang ZHANG,Gen-ze LI,Ting-yu LIU,Bo YU. Mechanism and modeling of car⁃following behavior under local multi⁃vehicle influence[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(3): 963-973.

Figures/Tables 12

Fig.1

Table 1

Vehicle driving state variables"

变量字段	字段含义	单位
$v s$	主车速度	$k m ? h - 1$
$v s p$	主车前车速度	$k m ? h - 1$
$Δ v s$	主车跟驰对速度差	$k m ? h - 1$
$v a$	相邻车道后车速度	$k m ? h - 1$
$v a p$	相邻车道前车速度	$k m ? h - 1$
$Δ v a$	相邻车道前、后速度差	$k m ? h - 1$
$Δ v s a$	主车与相邻车道后车纵向速度差	$k m ? h - 1$
$a s$	主车加速度	$m ? s - 2$
$a s p$	主车前车加速度	$m ? s - 2$
$a a$	相邻车道后车加速度	$m ? s - 2$
$a a p$	相邻车道前车加速度	$m ? s - 2$
$Δ x s$	主车道跟驰对间距	m
$Δ x a$	相邻车道前、后间距	m
$Δ x s a f$	主车与相邻车道后车纵向间距	m
$Δ x s a p$	主车与相邻车道前车纵向间距	m
$T$	跟驰轨迹时长	$s$

Table 1

Table 2

Correlation analysis of different vehicle variables between the subject and adjacent lane"

变量

对

Pearson相关系数

总体样本

前车为平稳序列

$v ˉ s ～$

$v ˉ a$

$Δ x ˉ s ～$

$Δ v ˉ a$

$v ˉ s ～$

$Δ x ˉ a$

$Δ x ˉ x ～$

$Δ x ˉ a$

Table 2

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Table 3

Variable data characteristic statistics"

变量	均值	标准差	最小值	较小四分位数	中位数	较大四分位数	最大值
$Δ v ˉ s a$	-2.22	6.68	-18.54	-6.46	-1.65	2.09	14.53
$v ˉ s$	38.70	5.52	24.72	34.59	38.64	42.92	51.99
$Δ x ˉ s a f$	148.77	122.91	0.53	28.37	125.86	248.83	462.83
$T$	249.21	88.99	136.00	178.00	220.00	298.00	476
$Δ v ˉ a$	0.64	1.38	-2.67	-0.25	00.44	1.55	4.73
$Δ x ˉ s$	21.77	6.28	8.76	16.83	20.75	26.51	34.92

Table 3

Fig.7

Fig.8

Fig.9

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Mechanism and modeling of car⁃following behavior under local multi⁃vehicle influence

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