Journal of Jilin University(Engineering and Technology Edition) ›› 2026, Vol. 56 ›› Issue (2): 431-442.doi: 10.13229/j.cnki.jdxbgxb.20240783

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Behavior control method of overtaking lane-changing in expressway interchanging weaving area

Jian-xiao MA(),Shuo HUAI,Yi ZHAO(),Ming-hao LI,Yu-xin CHEN,Si-yu ZHAO   

  1. College of Automobile and Traffic Engineering,Nanjing Forestry University,Nanjing 210037,China
  • Received:2024-07-16 Online:2026-02-01 Published:2026-03-17
  • Contact: Yi ZHAO E-mail:majx@njfu.edu.cn;zhaoyi207@126.com

Abstract:

This study focuses on the overtaking lane-changing behavior in urban expressway interchange weaving areas, aiming to analyze the lane-change space selection characteristics of overtaking vehicles and explore control methods for overtaking lane-change behavior. Utilizing real-time trajectory data, this study analyzes the differences in gap selection and lane-changing point selection across various stages of the overtaking process. Machine learning methods were used to predict lane-change duration and lane-change space selection changes. Based on the prediction results, a speed optimization control model for overtaking lane-change behavior was established. The control effect of the model was then tested using a cellular automata simulation environment. The results show that under the control model, the proportion of vehicles selecting "excellent" and "good" grade lane-change gaps and the optimal lane-change point position increased by up to 18.86 and 6.89 percentage points compared to actual values. Additionally, the operating speeds of the three lanes in the weaving area increased by 6.91%, 1.71%, and 3.85%, respectively, and the spatiotemporal utilization rates of the lanes also exhibited better balance.

Key words: interchange weaving area, overtaking lane-change behavior, lane-change gap, cellular automata, speed optimization control

CLC Number: 

  • U491

Fig.1

Field shooting of the expressway interchange weaving area"

Fig.2

Schematic diagram of weaving area and overtaking lane changing"

Fig. 3

Selection of lane-changing gap of a vehicle"

Table 1

Statistics of gap selection of overtaking lane-changing vehicle"

OLC第一阶段OLC第二阶段
RFRRBRFRRB
1/4位数22.082013.9217.5125.2514.44
中位数28.9028.8416.4229.7038.3822.32
3/4位数54.4072.2828.4571.0354.6148.02

Table 2

Rating table for vehicle lane-changing gap selection"

级别RBRRF
最大
中等
最小

Fig.4

Overtaking lane-changing gap selection evaluation diagram"

Fig.5

Percentage of lane change point location selection"

Table 3

Statistical data of vehicle lane change point selection (percentage of location)"

换道过程1/4位数中位数3/4位数
OLC第一阶段41.0860.0488.15
OLC第二阶段41.6651.8866.09

Fig.6

Overtaking lane-changing point location selection and crash risk distribution"

Fig.7

Scatterplot of lane change point location selection percentage"

Fig.8

Statistical histogram of vehicle lane-changing point selection"

Table 4

Vehicle parameter settings name comparison table(part)"

参 数含 义
m车辆纵向位置
n车辆横向位置
disFront与前方车辆距离
disRightFront与右前方车辆纵向距离
disRightBack与右后方车辆纵向距离
vFront前方车辆速度
vRightFront右前方车辆速度
vRightBack右后方车辆速度
v车辆当前速度
a车辆加速度
disBack与后方车辆距离
disLeftFront与左前方车辆纵向距离
disLeftBack与左后方车辆纵向距离
vBack后方车辆速度
vLeftFront左前方车辆速度
vLeftBack左后方车辆速度

Table 5

Random forest parameter setting table"

参 数取值
n_estimators200
max_depth10
min_samples_split2
min_samples_leaf1
max_featureslog2
random_state1
class_weightbalanced

Fig.9

Results of random forest prediction of vehicle lane-changing duration"

Fig.10

Deep neural network prediction model training and verification process"

Table 6

Deep neural network prediction model parameter selection"

参 数取值
隐藏层层数2
隐藏层神经元个数64
隐藏层激活函数ReLU
初始学习率0.001
epochs40
batch_size64
Dropout0.25
损失函数MSE

Table 7

Parameter settings for simulation"

变量参数设置对应实际值
元胞空间长度/cell3 000150 m
车道宽度/cell93.5 m
车道数/条55条
车辆最大速度/(cell×fps-11260 km/h
车身长度/cell904.5 m
仿真步长/fps9 0005 min

Fig.11

Comparison of gap selection for overtaking lane changing vehicles"

Fig.12

Comparison of gap control distribution of overtaking lane changing point selection"

Fig.13

Comparison of average speed in different lanes"

Fig.14

Comparison of spatiotemporal utilization rate of different lanes"

[1] Moridpour S, Sarvi M, Rose G. Lane changing models: a critical review[J]. Transportation Letters, 2010, 2(3): 157-173.
[2] 耿新力. 城区不确定环境下无人驾驶车辆行为决策方法研究[D]. 合肥: 中国科学技术大学信息科学技术学院,2017.
Geng Xin-li. Research on behavior decision-making approaches for autonomous vehicle in urban uncertainty environments[D]. Hefei: School of Information Science and Technology,University of Science and Technology of China, 2017.
[3] 张航, 段和柱, 储泽宇. 城市快速路互通立交交织区长度可靠性设计[J]. 重庆交通大学学报: 自然科学版, 2023, 42(3): 98-104.
Zhang Hang, Duan He-zhu, Chu Ze-yu. Reliability design of weaving segment length of urban expressway interchange[J]. Journal of Chongqing Jiaotong University (Natural Science),2013, 42(3): 98-104.
[4] 陈亮, 何志超, 李巧茹, 等. 多车道城市快速路交织区拥堵形成机制[J]. 中国安全科学学报, 2018, 28(6): 73-78.
Chen Liang, He Zhi-chao, Li Qiao-ru, et al. Study on congestion mechanism in multi-lane weaving section of urban expressway[J]. China Safety Science Journal, 2018, 28(6): 73-78.
[5] 李岩, 陈姜会, 曾明哲, 等. 考虑天气影响的高速公路交织区交通运行状态识别[J]. 交通运输系统工程与信息, 2023, 23(6): 111-119.
Li Yan, Chen Jiang-hui, Zeng Ming-zhe, et al. Identification of traffic operation status in freeway weaving segments considering weather effects[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(6): 111-119.
[6] 张卫华, 刘嘉茗, 解立鹏, 等. 混合网联环境快速路交织区交通流特性分析[J]. 东南大学学报: 自然科学版, 2023, 53(1): 156-164.
Zhang Wei-hua, Liu Jia-ming, Xie Li-peng, et al. Analysis on the characteristics of traffic flow in expressway weaving area under mixed connected and autonomous environment[J]. Journal of Southeast University (Natural Science Edition), 2023, 53(1): 156-164.
[7] Wang L, Abdel-Aty M, Shi Q, et al. Real-time crash prediction for expressway weaving segments[J]. Transportation Research Part C: Emerging Technologies, 2015, 61: 1-10.
[8] 谢济铭, 夏玉兰, 钱正富, 等. 考虑智能网联近邻车辆信息的交织区换道风险预警[J]. 交通运输工程学报, 2023, 23(2): 287-300.
Xie Ji-ming, Xia Yu-lan, Qian Zheng-fu, et al. Lane-change risk warning in interweaving area considering information from intelligent connected near-neighboring vehicles[J]. Journal of Traffic and Transportation Engineering, 2023, 23(2):287-300.
[9] 彭博, 王玉婷, 谢济铭, 等. 城市干线短交织区元胞自动机多级换道决策模型[J]. 交通运输系统工程与信息,2020,20(4):41-48.
Peng Bo, Wang Yu-ting, Xie Ji-ming, et al. Multi-stage lane changing decision model of urban trunk road's short weaving area based on cellular automata[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(4): 41-48.
[10] Zhao C W, Zhao Y, Wang Z Q, et al. Choice of lane-changing point in an urban intertunnel weaving section based on random forest and support vector machine[J]. Promet-Traffic & Transportation, 2023, 35(2): 161-174.
[11] Li M H, Zhao Y, Ma J X, et al. A study on the impact of overtaking lane-changing behavior in expressway interchange weaving areas[J]. Promet-Traffic & Transportation, 2024, 36(5): 973-987.
[12] 李珣, 马文哲, 赵征凡, 等. 车路协同下基于行车指引的改进STCA双车道换道模型[J]. 东南大学学报:自然科学版, 2020, 50(6): 1134-1142.
Li Xun, Ma Wen-zhe, Zhao Zheng-fan, et al. Improved STCA lane changing model for two-lane road based on driving guidance under CVIS[J]. Journal of Southeast University (Natural Science Edition), 2020, 50(6): 1134-1142.
[13] Zhou Z, Zhao Y, Li M H, et al. A causal inference-based speed control framework for discretionary lane-changing processes[J]. Journal of Transportation Engineering Part A—Systems, 2023, 49(8): 1-26.
[14] Yang D, Zheng S, Wen C, et al. A dynamic lane-changing trajectory planning model for automated vehicles[J]. Transportation Research Part C: Emerging Technologies, 2018, 95: 228-247.
[15] 吕伟, 黄广琛, 汪京辉. 基于元胞自动机的高速公路瓶颈交通演化仿真[J]. 交通运输系统工程与信息, 2022, 22(3): 293-302.
Lv Wei, Huang Guang-chen, Wang Jing-hui. Simulation of highway traffic bottleneck via cellular automata[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(3): 293-302.
[16] 张毅, 姚丹亚, 李力, 等. 智能车路协同系统关键技术与应用[J]. 交通运输系统工程与信息, 2021, 21(5): 40-51.
Zhang Yi, Yao Dan-ya, Li Li, et al. Technologies and applications for intelligent vehicle-infrastructure cooperation systems[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(5): 40-51.
[17] 黄玲, 郭亨聪, 张荣辉, 等. 人机混驾环境下基于LSTM的无人驾驶车辆换道行为模型[J]. 中国公路学报, 2020, 33(7): 156-166.
Huang Ling, Guo Heng-cong, Zhang Rong-hui, et al. LSTM-based lane changing behavior model for unmanned vehicle under environment of heterogeneous human-driven and autonomous vehicles[J]. China Journal of Highway and Transport, 2020, 33(7): 156-166.
[18] Luo Y, Xiang Y, Cao K, et al. A dynamic automated lane change maneuver based on vehicle-to-vehicle communication[J]. Transportation Research Part C: Emerging Technologies, 2016, 62: 87-102.
[19] 曲大义, 黑凯先, 郭海兵, 等. 车联网环境下车辆换道博弈行为及模型[J]. 吉林大学学报: 工学版, 2022, 52(1): 101-109.
Qu Da-yi, Kai-xian Hei, Guo Hai-bing, et al. Game behavior and model of lane-changing on the internet of vehicles environment[J]. Journal of Jilin University (Engineering and Technology Edition), 2022,52(1): 101-109.
[20] Wu Y, Abdel-Aty M, Zheng O, et al. Automated safety diagnosis based on unmanned aerial vehicle video and deep learning algorithm[J]. Transportation Research Record, 2020, 2674(8): 350-359.
[21] 王祺, 谢娜, 侯德藻, 等. 自适应巡航及协同式巡航对交通流的影响分析[J].中国公路学报, 2019, 32(6): 188-197.
Wang Qi, Xie Na, Hou De-zao, et al. Effects of adaptive cruise control and cooperative adaptive cruise control on traffic flow[J]. China Journal of Highway and Transport, 2019, 32(6): 188-197.
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