稀疏数据下的交通状态估计自适应卷积网络

doi:10.13229/j.cnki.jdxbgxb.20231247

摘要/Abstract

摘要：

卷积神经网络（CNN）是目前交通状态估计的深度学习算法中提取交通特征的关键模块，其对稀疏数据和多模式交通状态的计算不稳定，制约深度学习的状态估计精度。为进一步提升CNN的交通特征分析精度，本文建立了一种编码-解码的交通自适应卷积网络。首先，本文所提网络构建一种交通特征编码CNN，采用下采样操作聚合邻域交通信息，以从稀疏数据中提取有效的交通特征；其次，构建交通状态自适应重构CNN，利用提取的特征准确重构不同模式的交通状态。为表征多样化交通状态时空结构，该CNN引入先验知识引导卷积核形变。最后，实验采用长春市出租车GPS数据对算法在稀疏数据和不同情景下的交通状态估计性能进行验证。实验结果表明，与LSTM、GAN等先进算法相比，本文提出的改进的CNN算法估计精度提高了6.05 km/h RMSE，同时在不同交通情景下的估计精度仅差3.34% RMSE，能为深度学习估计交通状态提供有力的交通特征分析支撑。

关键词: 交通管理工程, 交通状态估计, 稀疏数据, 卷积神经网络, 编码-解码结构

Abstract:

Convolutional neural networks （CNN） are the key modules for extracting traffic features in deep learning algorithms for traffic state estimation. However， their computational instability on sparse data and multi-modal traffic states limits the accuracy of deep learning for state estimation. To improve the accuracy of CNN-based traffic feature analysis， this paper proposed an encode-decode traffic adaptive convolutional network. Firstly， the proposed network constructed a traffic feature encoding CNN， which uses down-sampling operations to aggregate neighboring traffic information and extracts effective traffic features from sparse data. Secondly， a traffic state adaptive reconstruction CNN is constructed to utilize the extracted features to accurately reconstruct different patterns of traffic states. To represent diverse spatio-temporal structure of traffic states， this CNN introduced prior knowledge to guide the deformation of convolutional kernels. Finally， the performance of algorithm in traffic state estimation under sparse data and different scenarios was validated using taxi GPS data in Changchun City. Experimental results show that compared with advanced algorithms such as LSTM and GAN， the improved CNN alogorithm in this paper improves the estimation accuracy by 6.05 km/h RMSE. Besides， the proposed algorithm has an accuracy difference of only 3.34% RMSE in different traffic scenarios， can provide robust traffic festure analysis support for deep learning-based traffic state estimation.

Key words: traffic management engineering, traffic state estimation, sparse data, convolutional neural network, encode-decode structure

中图分类号:

U491

田婧,马社强,宋现敏,赵丹,陈发城. 稀疏数据下的交通状态估计自适应卷积网络[J]. 吉林大学学报(工学版), 2025, 55(8): 2579-2587.

Jing TIAN,She-qiang MA,Xian-min SONG,Dan ZHAO,Fa-cheng CHEN. Adaptive convolutional network for traffic state estimation under sparse data[J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(8): 2579-2587.

图/表 9

图1

图2

图3

图4

图5

图6

图7

表1

图8

参考文献 28

[1]	Yu J J Q, Gu J. Real-time traffic speed estimation with graph convolutional generative autoencoder[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(10): 3940-3951.
[2]	Zhang L, Chen T, Yu B, et al. Suburban demand responsive transit service with rental vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(4): 2391-2403.
[3]	Li X, Wang T, Xu W, et al. A novel model for designing a demand-responsive connector (DRC) transit system with consideration of users' preferred time windows[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(4): 2442-2451.
[4]	田婧. 知识感知下的道路交通状态重构方法研究[D]. 长春: 吉林大学交通学院, 2023.
	Tian Jing. Research on road traffic state reconstruction method in perspective of knowledge-aware[D]. Changchun: School of Transportation, Jilin University, 2023.
[5]	Ahmed M S, Cook A R. Analysis of freeway traffic time-series data by using box-jenkins techniques[J]. Transportation Research Record, 1979, 722: 1-9.
[6]	Williams B M, Hoel L A. Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results[J]. Journal of Transportation Engineering, 2003, 129(6): 664-672.
[7]	陈喜群,曹震,莫栋. 融合卡尔曼滤波的高速公路状态估计误差界限分析[J]. 交通运输系统工程与信息, 2022, 22(4): 72-78.
	Chen Xi-qun, Cao Zhen, Mo Dong. Analyzing error bounds of highway traffic state estimation via Kalman filter fusion[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(4): 72-78.
[8]	Tang J, Zhang G, Wang Y, et al. A hybrid approach to integrate fuzzy C-means based imputation method with genetic algorithm for missing traffic volume data estimation[J]. Transportation Research Part C: Emerging Technologies, 2015, 51(4): 29-40.
[9]	陈佳良,胡钊政,李飞. 基于时空特征序列匹配的交通流状态估计方法[J]. 交通信息与安全, 2021, 39(3): 68-76.
	Chen Jia-liang, Hu Zhao-zheng, Li Fei. An estimation method of traffic flow state based on matching of temporal-spatial feature sequences[J]. Journal of Transport Information and Safety, 2021, 39(3): 68-76.
[10]	Xiao J, Wei C, Liu Y. Speed estimation of traffic flow using multiple kernel support vector regression[J]. Physica A: Statistical Mechanics and its Applications, 2018, 509(11): 989-997.
[11]	Chen X, He Z, Sun L. A bayesian tensor decomposition approach for spatiotemporal traffic data imputation[J]. Transportation Research Part C: Emerging Technologies, 2019, 98: 73-84.
[12]	Tian Y, Zhang K, Li J, et al. LSTM-based traffic flow prediction with missing data[J]. Neurocomputing (Amsterdam), 2018, 318: 297-305.
[13]	Zhang S, Zhou L, Chen X, et al. Network‐wide traffic speed forecasting: 3D convolutional neural network with ensemble empirical mode decomposition[J]. Computer-Aided Civil and Infrastructure Engineering, 2020, 35(10): 1132-1147.
[14]	Rempe F, Franeck P, Bogenberger K. On the estimation of traffic speeds with deep convolutional neural networks given probe data[J]. Transportation Research Part C: Emerging Technologies, 2022, 134(1): 103448.
[15]	Ouafa B, Bilal T T, Hwasoo Y, et al. Traffic data imputation using deep convolutional neural networks[J]. IEEE Access, 2020, 8: 104740-104752.
[16]	Dai J, Qi H, Xiong Y, et al. Deformable convolutional networks[C]∥Proceeding of the IEEE International Conference on Computer Vision. Los Alamitos: IEEE Computer Society, 2017: 764-773.
[17]	Zhu X, Hu H, Lin S, et al. Deformable convnets V2: More deformable, better results[C]∥Proceeding of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Los Alamitos: IEEE Computer Society, 2020: 9300-9308.
[18]	Doersch C. Tutorial on variational autoencoders[J]. Arxiv Preprint, 2016, 6: 160605908.
[19]	Dilip D M, Freris N M, Jabari S E. Sparse estimation of travel time distributions using Gamma kernels[C]∥Transportation Research Board 96th Annual Meeting. Washington: Transportation Research Board, 2017: 01628670.
[20]	Jabari S E G, Dilip D M, Lin D, et al. Learning traffic flow dynamics using random fields[J]. IEEE Access, 2019, 7: 130566-130577.
[21]	Treiber M, Kesting A, Wilson R E. Reconstructing the traffic state by fusion of heterogeneous data[J]. Computer-Aided Civil and Infrastructure Engineering, 2011, 26(6): 408-419.
[22]	Scherer D, Müller A, Behnke S. Evaluation of pooling operations in convolutional architectures for object recognition[C]∥Lecture Notes in Computer Science. Berlin: Springer, 2010: 92-101.
[23]	Tan C P, Yao J R, Tang K S, et al. Cycle-based queue length estimation for signalized intersections using sparse vehicle trajectory data[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(1): 91-106.
[24]	Masci J, Meier U, Ciresan D, et al. Stacked convolutional auto-encoders for hierarchical feature extraction[C]∥Proceeding of the International Conference on Artificial Neural Networks. Berlin: Springer, 2011: 52-59.
[25]	Chen X, Yang J, Sun L. A nonconvex low-rank tensor completion model for spatiotemporal traffic data imputation[J]. Transportation Research Part C: Emerging Technologies, 2020, 117(8): 102673.
[26]	Cui Z, Ke R, Pu Z, et al. Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values[J]. Transportation Research Part C: Emerging Technologies, 2020, 118(9): 102674.
[27]	Chen Y, Lv Y, Wang F Y. Traffic flow imputation using parallel data and generative adversarial networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(4): 1624-1630.
[28]	Zhang K, He Z, Zheng L, et al. A generative adversarial network for travel times imputation using trajectory data[J]. Computer-Aided Civil and Infrastructure Engineering, 2021, 36(2): 197-212.

相关文章 15

[1]	肖红,刘显德. 基于混合智能的学习状态实时采集与动态分析方法[J]. 吉林大学学报(工学版), 2025, 55(7): 2402-2408.
[2]	冯志刚,王首起,于明月. 基于变分模态提取及轻量级网络的滚动轴承故障诊断[J]. 吉林大学学报(工学版), 2025, 55(6): 1883-1891.
[3]	杜睿山,王紫珊. 基于时空注意力的多视角人脸表情识别算法[J]. 吉林大学学报(工学版), 2025, 55(6): 2097-2102.
[4]	胡宏宇,张争光,曲优,蔡沐雨,高菲,高镇海. 基于双分支和可变形卷积网络的驾驶员行为识别方法[J]. 吉林大学学报(工学版), 2025, 55(1): 93-104.
[5]	汪豪,赵彬,刘国华. 基于时间和运动增强的视频动作识别[J]. 吉林大学学报(工学版), 2025, 55(1): 339-346.
[6]	张锦洲,姬世青,谭创. 融合卷积神经网络和双边滤波的相贯线焊缝提取算法[J]. 吉林大学学报(工学版), 2024, 54(8): 2313-2318.
[7]	朱圣杰,王宣,徐芳,彭佳琦,王远超. 机载广域遥感图像的尺度归一化目标检测方法[J]. 吉林大学学报(工学版), 2024, 54(8): 2329-2337.
[8]	赵宏伟,武鸿,马克,李海. 基于知识蒸馏的图像分类框架[J]. 吉林大学学报(工学版), 2024, 54(8): 2307-2312.
[9]	特木尔朝鲁朝鲁,张亚萍. 基于卷积神经网络的无线传感器网络链路异常检测算法[J]. 吉林大学学报(工学版), 2024, 54(8): 2295-2300.
[10]	孙铭会,薛浩,金玉波,曲卫东,秦贵和. 联合时空注意力的视频显著性预测[J]. 吉林大学学报(工学版), 2024, 54(6): 1767-1776.
[11]	魏晓辉,王晨洋,吴旗,郑新阳,于洪梅,岳恒山. 面向脉动阵列神经网络加速器的软错误近似容错设计[J]. 吉林大学学报(工学版), 2024, 54(6): 1746-1755.
[12]	夏超,王梦佳,朱剑月,杨志刚. 基于分层卷积自编码器的钝体湍流流场降阶分析[J]. 吉林大学学报(工学版), 2024, 54(4): 874-882.
[13]	杨国俊,齐亚辉,石秀名. 基于数字图像技术的桥梁裂缝检测综述[J]. 吉林大学学报(工学版), 2024, 54(2): 313-332.
[14]	陆宇,陈谦,殷海兵. 基于卷积神经网络的视频编码优化算法[J]. 吉林大学学报(工学版), 2024, 54(11): 3296-3301.
[15]	张玺君,尚继洋,余光杰,郝俊. 基于注意力的多尺度卷积神经网络轴承故障诊断[J]. 吉林大学学报(工学版), 2024, 54(10): 3009-3017.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

交通情景	稀疏率误差指标	20%	30%	40%	50%	55%
路段	RMSE	8.85	7.95	4.80	4.67	4.15
	MAE	6.77	6.18	3.71	2.60	3.06
	NMSE	0.12	0.10	0.05	0.05	0.05
交叉口	RMSE	9.02	8.74	5.20	4.67	3.84
	MAE	7.24	6.90	4.04	2.36	2.87
	NMSE	0.17	0.16	0.07	0.05	0.05