基于双向长短期记忆网络的城市快速路合流区车速预测

谢济铭; 夏玉兰; 秦雅琴; 赵荣达; 刘兵; 段国忠; 陈金宏

doi:10.3969/j.issn.0258-2724.20220005

基于双向长短期记忆网络的城市快速路合流区车速预测

doi: 10.3969/j.issn.0258-2724.20220005

1.
昆明理工大学交通工程学院，云南昆明 650224
2.
云南省交通投资建设集团有限公司，云南昆明 650103

基金项目: 国家重点研发计划（2018YFB1600500）；国家自然科学基金（71861016）

详细信息

作者简介:
谢济铭（1994—），男，博士生，研究方向为智能车辆运动决策与路径规划，E-mail：xiejiming@stu.kust.edu.cn

通讯作者:
秦雅琴（1972—），女，博士，教授，研究方向为车路协同系统建模与优化，E-mail：qinyaqin@kust.edu.cn

中图分类号: U491.1
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- 被引次数: 0
出版历程
- 收稿日期: 2022-01-03
- 修回日期: 2022-05-25
- 网络出版日期: 2024-07-13

Traffic speed prediction in merging zone of urban expressway based on bidirectional long short-term memory network

1.
Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650504, China
2.
Yunnan Communications Investment & Construction Group CO., LTD. Kunming 650103, China

摘要

摘要:
非典型复杂场景微观交通参数的准确预测，是保证车路协同系统(IVICS)稳定运行的前提. 首先，基于无人机高空视频，从广域视角提取交织区高峰时段全样本高精度车辆轨迹数据；然后，考虑双向长短期记忆网络时间较长且人工设置训练参数对模型预测性能影响较大，针对实例合流区高峰时段车速离散、分布紊乱、瓶颈现象等独特特性，提出基于贝叶斯超参数(Bayesian hyperparameters optimization)优化的BHO-Bi-LSTM (BHO-Bi-directional long short term memory)车速预测集成模型；最后，构建经典多元线性回归车速预测模型、Bi-LSTM车速预测模型作为对比. 结果表明：BHO-Bi-LSTM模型表现最优，拟合优度、秩相关度分别为91.05%、94.87%，误差均值、误差的标准差、均方误差、均方根误差、归一化均方根误差分别为0.0561、0.4556、0.2106、0.4589、0.0785，有效改善了合流区高峰时段车速特性复杂而导致不易预测的缺陷.
- 交通工程 /
- 速度预测 /
- 多车道交织区 /
- 轨迹数据 /
- 贝叶斯优化
Abstract:
Accurate prediction of microscopic traffic parameters in atypical complex scenes is a prerequisite to ensure stable operation of the intelligent vehicle infrastructure cooperative systems (IVICS). First, using the UAV video, the full-sample high-precision vehicle trajectory data of the intertwined area during peak hours are extracted from a wide-area view. Then, as bidirectional long and short-term memory networks cost long time and affect the prediction performance of the model when training parameters are manually set, a BHO-Bi-LSTM (BHO-Bi-directional long short-term memory) integrated vehicle speed prediction model based on Bayesian hyperparameters optimization is proposed for solving the discrete and disordered distribution and bottleneck of vehicle speeds during the peak hours in the merging area of interest. Finally, the classical multiple linear regression model and Bi-LSTM model of vehicle speed prediction are constructed for comparison. The results show that the BHO-Bi-LSTM model outperforms other models, with a goodness-of-fit and rank correlation of 91.05% and 94.87%, respectively, and error mean, error standard deviation, mean square error, root mean square error, and normalized root mean square error of 0.0561, 0.4556, 0.2106, 0.4589, and 0.0785, respectively, which can overcome the disadvantage in prediction of complicated traffic speeds during peak hours.
- traffic engineering /
- speed prediction /
- multiple weaving area /
- trajectory data /
- Bayesian optimization

HTML全文

图 1 模型框架

Figure 1. Model frame

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图 2 高空视频拍摄场景

Figure 2. Video shooting scene at high altitude

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图 3 数据提取过程

Figure 3. Data extraction process

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图 4 微观轨迹信息提取结果

Figure 4. Results of microscopic trajectory information extraction

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图 5 平（高）峰时段交织区车速-位置分布曲线

Figure 5. Vehicle speed-position distribution curves in weaving area during flat （high） peak hours

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图 6 平（高）峰时段实例合流区车速分布

Figure 6. Distribution of traffic speeds in merging area during peak hours

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图 7 贝叶斯超参数优化过程

Figure 7. Bayesian hyperparameter optimization process

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图 8 交叉实验训练结果

Figure 8. Cross-experiment training results

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图 9 R²对比图

Figure 9. R² comparison

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图 10 误差柱形图

Figure 10. Error bar graphs

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图 11 逐样本误差点线图

Figure 11. Sample-wise errors

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图 12 r_s对比图

Figure 12. Rank correlation comparison

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表 1 模型总体评价指标对比

Table 1. Comparison of overall model evaluation indicators

模型	R²	Error	Error StD	MSE	RMSE	NRMSE	r_s
多元线性回归^[22] （模型Ⅰ）	79.80%	0.0017	1.6595	2.7542	1.6596	0.2690	84.40%
Bi-LSTM^[23] （模型Ⅱ）	88.82%	−0.0053	0.4711	0.2218	0.4709	0.0806	94.53%
BHO-Bi-LSTM （模型Ⅲ）	91.05%	0.0561	0.4556	0.2106	0.4589	0.0785	94.87%
模型Ⅲ-模型Ⅰ （提升或下降比例）	↑11.25%	+ 0.0544 （↓3200%）	−1.2039 （↑72.55%）	−2.5436 （↑92.35%）	−1.2007 （↑72.35%）	−0.1905 （↑70.82%）	↑10.43%
模型Ⅲ-模型Ⅱ （提升或下降比例）	↑2.23%	+ 0.0614 （↓1158.49%）	−0.0155 （↑3.29%）	−0.0112 （↑5.05%）	−0.012 （↑2.548%）	−0.0021 （↑2.605%）	↑0.34%
黑体加粗表示最优指标

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