基于WT-WOA的城市快速路交通震荡吸收策略

赵红亮; 张兆磊; 易可夫; 吴伟; 郭静

doi:10.3963/j.jssn.1674-4861.2025.01.016

基于WT-WOA的城市快速路交通震荡吸收策略

doi: 10.3963/j.jssn.1674-4861.2025.01.016

赵红亮^1,,
张兆磊¹,
易可夫^1, ,,
吴伟²,
郭静¹

1.
长沙理工大学交通学院长沙 410114
2.
重庆交通大学交通运输学院重庆 400074

基金项目:

国家留学基金委国际合作项目 2023-21

湖南省重点研发计划项目 2023SK2052

教育部人文社会科学研究项目 22YJCZH189

长沙市科技计划项目 kh2202002

详细信息

作者简介:
赵红亮（1999—），硕士研究生. 研究方向：交通流理论、智能交通. E-mail：cslg_zhl@163.com

通讯作者:
易可夫（1987—），博士，副教授. 研究方向：智能交通. E-mail：corfyi@csust.edu.cn

中图分类号: U491
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- 文章访问数: 15
- HTML全文浏览量: 6
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- 被引次数: 0
出版历程
- 收稿日期: 2024-09-19
- 网络出版日期: 2025-06-27

Traffic Oscillation Absorption Strategy of Urban Expressway Based on WT-WOA

1.
School of Transportation, Changsha University of Science & Technology, Changsha 410114, China
2.
School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China

摘要

摘要: 城市道路交通瓶颈点的交通震荡是诱发交通事故、通行延误和增加能源消耗的主要原因，缓解交通震荡可以显著提升交通运行效率和安全。为精确获取交通震荡的周期，研究了基于小波变换（wavelet transform，WT）的交通波时频分析方法，并开发了基于鲸鱼优化算法（whale optimization algorithm，WOA）的小波参数自适应标定方法。通过构建以交通震荡起止时间的识别误差绝对值为适应度函数，采用全局搜索机制克服局部最优问题，动态优化小波变换的尺度系数与平移系数，克服了小波变换易陷入局部最优的缺点，并解决了传统交通震荡识别方法中由于判别参数在阈值上下波动，导致识别不准确或误判的问题。在此基础上，提出融合能源消耗和驾驶安全的多目标协同交通波吸收控制框架，通过建立以燃油消耗率和交通安全指标的多目标优化函数，设计基于速度引导的车辆准入控制机制，在瓶颈区域上游实施动态速度调控，通过优化部分车辆行驶速度，减少进入交通瓶颈区的车辆数量，从而加快交通震荡的消散，抑制频繁加减速导致的能源损耗和安全风险。研究结果表明：在道路瓶颈区实施交通波吸收方法后，碰撞持续时间和综合碰撞时间分别降低了73.86%和61.07%，燃油消耗降低16.15%；分析网联自动驾驶车辆渗透率变化对控制方法影响发现，能耗和安全风险随渗透率增加而减小，渗透率≥0.3时，控制方法效果显著，能耗与安全风险都显著降低。
- 交通工程 /
- 交通震荡 /
- 小波变换 /
- 交通波吸收 /
- 交通仿真 /
- 低碳交通
Abstract: Traffic oscillations are primary causes of traffic accidents, delays, and increased energy consumption at bottlenecks of urban road networks. Mitigating oscillations can significantly improve traffic efficiency and safety. A wavelet transform-based time-frequency analysis method is used to accurately capture the cycle of traffic oscillations. Additionally, an adaptive wavelet parameter calibration method is developed based on the whale optimization algorithm (WOA). A fitness function is set up based on the absolute error in identifying the start and end times of traffic shockwaves. In further, a global search mechanism is introduced to overcome the issue of local optima, dynamically optimizing the scale and translation coefficients of the wavelet transform. This approach addresses the common problem of wavelet transforms getting trapped in local optima, and the inaccuracies or misjudgments caused by fluctuations in the discriminative parameters around threshold values in traditional traffic oscillations identification methods. Based on this, a multi-objective collaborative traffic wave absorption control framework integrating energy consumption and driving safety is proposed. A multi-objective optimization function is then investigated incorporating fuel consumption rate and traffic safety indicators. A speed-guided vehicle access control mechanism is designed with dynamic speed regulation implemented upstream of the bottleneck area. By optimizing the speed of specific vehicles, the number of vehicles entering the bottleneck is reduced, accelerating the dissipation of traffic oscillations and suppressing energy loss and safety risks caused by frequent acceleration and deceleration. The results indicate that collision duration and overall collision time decreased by 73.86% and 61.07% respectively, while fuel consumption was reduced by 16.15%, after implementing the traffic wave absorption method in the bottleneck area. The energy consumption and safety risks decrease as the penetration rate increases by analysis of the impact of changes in the penetration rate of connected and autonomous vehicles on the control method. When the penetration rate reaches 0.3 or higher, the control method becomes significantly more effective, with notable reductions in both energy consumption and safety risks.
- traffic engineering /
- traffic oscillations /
- wavelet transform /
- traffic wave absorption /
- traffic simulation /

HTML全文

图 1 基于WT-WOA的交通震荡识别图

Figure 1. Taffic oscillation identification diagram based on WT-WOA

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图 2 JAD控制策略示意图

Figure 2. Schematic diagram of JAD control strategy

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图 3 不同吸收车辆轨迹图

Figure 3. Vehicle trajectories with different absorption absorbing vehicle

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图 4 后车控制前后速度变化图

Figure 4. Speed change diagram before and after rear vehicle control

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图 5 不同吸收车辆行程时间图

Figure 5. Diagram of vehicle travel time with different absorption

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图 6 不同吸收车辆能耗指标对比图

Figure 6. Comparison chart of energy consumption indicators of different absorption vehicles

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图 7 优策略下部分车辆震荡区域延误图

Figure 7. Delay map of some vehicle shock areas under the optimal strategy

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图 8 控制效果对比图

Figure 8. Control effect comparison chart

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图 9 不同渗透率下不同吸收车辆的能耗和排放曲线

Figure 9. Energy consumption and emission curves of different absorption vehicles under different penetration rate

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图 10 不同渗透率下车辆安全控制效果

Figure 10. Vehicle safety control effect under different penetration rates

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图 11 不同车头时距下的车辆燃油消耗

Figure 11. Vehicle fuel consumption at different headways

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表 1 选取不同吸收车辆及速度的控制策略对安全指标的影响

Table 1. The impact of different absorption vehicle control strategies on safety indicators

吸收车辆	TET	TIT	吸收速度	J排序	吸收车辆	TET	TIT	吸收速度	J排序
1	2.08	0.09	15.96	10	6	55.87	50.42	36.44	4
2	33.90	46.18	24.41	8	7	47.73	41.50	38.32	5
3	73.86	61.07	25.79	1	8	44.32	38.57	38.91	6
4	70.45	59.73	30.71	2	9	40.34	36.03	41.11	7
5	61.17	50.41	33.08	3	10	36.36	31.53	42.34	9
注：吸收速度的单位为km/h；J排序为不同控制策略下的适用度函数值排序

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表 2 不同控制策略下的评估结果

Table 2. Evaluation results under different control strategies

控制类型	本策略	WT-JAD	完全JAD	无控制
行程时间/s	2 616.5	2 618.3	2 626.2	2 648.9
总延误/s	665.3	665.9	665.9	665.9
平均PM_X/mg	0.362	0.370	0.447	0.490
平均NO_X/mg	0.305	0.313	0.332	0.354
平均油耗/ml	661.019	692.595	730.189	788.319
平均CO/mg	18.615	20.133	21.768	24.870
注：基于小波变换的JAD策略、完全消除交通震荡的JAD策略在图表中命名分别简化为WT-JAD策略、完全JAD策略

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表 3 最优交通震荡吸收策略下各车辆在震荡区的行程时间

Table 3. Travel time in the oscillation zone under the traffic oscillation mitigation strategy

车辆	无控制/s	本策略/s	车辆	无控制/s	本策略/s
头车	36.7	36.7	后车5	34.3	34.1
后车1	36	36	后车6	33.9	33.4
后车2	34.4	34.4	后车7	34	33.3
后车3	34.8	35	后车8	34.3	33.6
后车4	34.2	34.2	后车9	34	33.1

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