Journal of Jilin University(Engineering and Technology Edition) ›› 2026, Vol. 56 ›› Issue (7): 1834-1844.doi: 10.13229/j.cnki.jdxbgxb.20250552

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Lane⁃changing model for connected and automated vehicle aggregation considering driving styles

Tian-jun FENG1(),Yan-ming HAO1,Fei-yan LI1,He-yao GAO1,Yi-xian LIU1,Nan LIU1,Jin-feng LI2   

  1. 1.School of Transportation Science and Engineering，Jilin Architecture University，Changchun 130018，China
    2.School of Transportation，Changchun Architecture College，Changchun 130607，China
  • Received:2025-06-24 Online:2026-07-01 Published:2026-08-12

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Abstract:

Considering that a large number of CAV and HV will mix in the future， an agglomeration lane-changing model considering drivers' driving styles was proposed to enable CAV vehicles to achieve local agglomeration， promote traffic flow homogenization， and enhance traffic operation efficiency. The influence of driving styles in agglomeration was investigated， and the effects of aggressive， conservative， and autonomous vehicle driving styles on the three agglomeration lane-changing models CDA， CVA， and NOA were quantitatively analyzed. The agglomeration lane-changing model is based on the NGSIM database， and vehicle driving styles were classified using principal component analysis and K-means analysis， and finally simulated using Matlab to analyze the road capacity under the three lane-changing models. The research results indicate that the application of the lane-changing model and the increase in the penetration rate of aggressive drivers effectively improved road capacity， with the two roads studied showing a maximum increase of more than 7% in capacity. The increase in the proportion of conservative drivers improved road traffic flow stability and safety levels， while the increase in CAV penetration rate significantly improved road traffic flow stability and safety levels while also improving road capacity.

Key words: engineering of communication and transportation, elementary cellular automaton, agglomeration lane-changing model, driving style, autonomous driving, heterogeneous traffic flow

CLC Number: 

  • U491.112

Fig.1

Logic diagram for agglomeration lane changing strategy"

Fig.2

Data smoothing effect"

Table 1

Extracting driving style indicators"

变量单位变量单位
V_meanm/svar_decm/s2
V_maxm/sT_meanm
A_meanm/s2var_Tm
D_meanm/s2lane_changes-
var_accm/s2

Table 2

Component matrix"

变量成分
123
A_mean0.893 8900.133 9800.218 912
D_mean-0.619 719-0.652 8690.335 879
var_acc0.855 4960.294 6550.172 866
var_dec0.610 8860.698 697-0.291 248
var_T-0.529 0640.636 0360.520 895
V_mean0.725 196-0.413 6680.213 898
T_mean-0.578 8010.635 2560.479 241
V_max0.574 072-0.372 4730.583 595
Lane_changes0.350 8480.042 0360.300 216

Table 3

Total variance explained"

成分总计方差百分比累积%
13.88243.12943.129
22.13923.76666.895
31.25313.92180.816
40.90310.03290.848
50.3724.13594.983
60.2893.21198.194
70.0720.79398.989
80.0480.52899.517
90.0430.483100.000

Fig.3

Principal component analysis （PCA） plot"

Fig.4

Two-dimensional presentation of principal component analysis plots"

Table 4

Variant meaning of code"

代码实际意义代码实际意义
A平均减速度F平均速度
B时距方差G平均加速度
C最大速度H加速度方差
D平均车头时距I减速度方差
E换道次数

Table 5

Final clustering center"

种类聚类
12
an-1.732 6740.152 507
bn1.919 921-0.168 988
gsafe1.384 303-0.121 844

Table 6

Relationship between clustering centers and original variables"

变量聚类中心
类别1类别2
A_mean-0.988 548 407 944 000.087 010 356 262 00
D_mean0.285 272 402 594 00-0.025 109 299 837 00
var_acc-0.677 282 431 651 000.059 613 284 428 00
var_dec-0.120 198 726 371 000.010 579 803 878 00
var_T2.858 910 821 477 00-0.251 636 345 396 00
V_mean-1.754 638 491 238 000.154 440 206 444 00
T_mean2.885 929 532 673 00-0.254 014 485 439 00
V_max-0.901 926 053 876 000.079 385 916 648 00
Lane_changes-0.111 609 498 948 000.009 823 678 064 00

Table 7

Vehicle simulation parameters"

种类CAV激进型HV保守型HV
vmax202015
an442
bn332
τ00.30.5
pslow00.40.2
gsafe11.753.11

Fig.5

Traffic flow for three lane-changing strategies with different shares of aggressive styles"

Fig.6

Comparison of average capacity for differentshares of aggressive drivers"

Fig.7

Under CDV aggregation lane change strategy， when vehicle density is 50 veh/km， spatio-temporal mapof road traffic flow under different CAV penetration rates"

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