计算机科学 ›› 2020, Vol. 47 ›› Issue (5): 84-89.doi: 10.11896/jsjkx.190100213
熊亭1, 戚湧1, 张伟斌2
XIONG Ting1, QI Yong1, ZHANG Wei-bin2
摘要: 随着城市化进程的加快,我国城市机动车数量快速增加,使得现有路网容量难以满足交通运输需求,交通拥堵、环境污染、交通事故等问题与日俱增。准确高效的交通流预测作为智能交通系统的核心,能够有效解决交通出行和管理方面的问题。现有的短时交通流预测研究往往基于浅层的模型方法,不能充分反映交通流特性。文中针对复杂的交通网络结构,提出了一种基于DCGRU-RF(Diffusion Convolutional Gated Recurrent Unit-Random Forest)模型的短时交通流预测方法。首先,使用DCGRU(Diffusion Convolutional Gated Recurrent Unit)网络刻画交通流时间序列数据中的时空相关性特征;在获取数据中的依赖关系和潜在特征后,选择RF(Random Forest)模型作为预测器,以抽取的特征为基础构建非线性预测模型,得出最终的预测结果。实验以两条城市道路中的38个检测器为实验对象,选取了5周工作日的交通流数据,并将所提方法与其他常见交通流量预测模型进行比较。结果表明,DCGRU-RF模型能够进一步提高预测精度,准确度可达95%。
中图分类号:
[1]YANG D I,WU J P,ZHANG Q S.The development of intelligent transportation system (ITS) and its model research [J].Journal of Beijing University of Aeronautics and Astronautics,2000,26(1):22-25. [2]LV Y S,DUAN Y J,et al.Traffic Flow Prediction With Big Data:A Deep Learning Approach[J].IEEE,2015,16(2):865-873. [3]LEVIN M,TSAO Y D.On forecasting freeway occupancies and volumes[J].Transportation Research Record,1980,773:47-49. [4]VASANTHAKUMAR S,VANAJAKSHI L.Short-term traffic flow prediction using seasonal ARIMA model with limited input data[J].European Transport Research Review,2015,7(3):21. [5]GUO J,HUANG W,WILLIAMS B M.Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification[J].Transportation Research Part C:Emerging Technologies,2014,43:50-64. [6]WU C H,HO J M,LEE D T.Travel-time prediction with support vectorregression[J].IEEE transactions on intelligent transportation systems,2004,5(4):276-281. [7]HOU Y,EDARA P,SUN C.Traffic flow forecasting for urban work zones[J].IEEE Transactions on Intelligent Transportation Systems,2015,16(4):1761-1770. [8]Xiaolei M,Zhuang D,Zhengbing H,et al.Learning Traffic as Images:A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction[J].Sensors,2017,17(4):818. [9]HU W,YAN L,LIU K,et al.A short-term traffic flow forecasting method based on the hybrid PSO-SVR[J].NeuralProces-sing Letters,2016,43(1):155-172. [10]CHEN X B,LIU X,WEI Z J,et al.Short-term Traffic FlowForecasting of Road Network Based on GA-LSSVR Model[J].Journal of Transportation Systems Engineering and Information Technology,2016,17(1):60-66. [11]WU Y,TAN H.Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework[J].arXiv:1612.01022. [12]BENGIO Y.Learning Deep Architectures for AI[J].Foundations & Trends® in Machine Learning,2009,2(1):1-127. [13]BREIMAN L.Random Forests[J].Machine Learning,2001,45(1):5-32. [14]LI Y,YU R,SHAHABI C,et al.Diffusion convolutional recurrent neural network:Data-driven traffic forecasting[J].arXiv:1707.01926,2017. [15]CHO K, MERRIENBOER B V,GULCEHRE C,et al.Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation[J].arXiv:1406.1078. [16]LIPPI M,BERTINI M,FRASCONI P.Short-Term Traffic Flow Forecasting:An Experimental Comparison of Time-Series Analysis and Supervised Learning[J].IEEE Transactions on Intelligent Transportation Systems,2013,14(2):871-882. [17]PEARSON,KARL.The Problem of the Random Walk[J].Nature,1905,72(1865):294-294. [18]WANG D,ZHANG Q,WU S Y.Traffic Flow Forecast with Urban Transport Network [C]//2016 IEEE InternationalConfe-rence on Intelligent Transportation Engineering.2016:139-143. [19]LUO X L,JIAO Q Q,NIU L Y,et al.Short-term traffic flow prediction based on deep learning[J].Application Research of Computers,2017,34(1):91-95. [20]BAI S,KOLTER J Z,KOLTUN V.An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling[J].arXiv:1803.01271. [21]ZHENG Z,PAN L,PHOLSENA K.Mode Decomposition Based Hybrid Model for Traffic Flow Prediction[C]//2018 IEEE Third International Conference on Data Science in Cyberspace (DSC).IEEE,2018:521-526. |
[1] | 徐涌鑫, 赵俊峰, 王亚沙, 谢冰, 杨恺. 时序知识图谱表示学习 Temporal Knowledge Graph Representation Learning 计算机科学, 2022, 49(9): 162-171. https://doi.org/10.11896/jsjkx.220500204 |
[2] | 饶志双, 贾真, 张凡, 李天瑞. 基于Key-Value关联记忆网络的知识图谱问答方法 Key-Value Relational Memory Networks for Question Answering over Knowledge Graph 计算机科学, 2022, 49(9): 202-207. https://doi.org/10.11896/jsjkx.220300277 |
[3] | 汤凌韬, 王迪, 张鲁飞, 刘盛云. 基于安全多方计算和差分隐私的联邦学习方案 Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy 计算机科学, 2022, 49(9): 297-305. https://doi.org/10.11896/jsjkx.210800108 |
[4] | 王剑, 彭雨琦, 赵宇斐, 杨健. 基于深度学习的社交网络舆情信息抽取方法综述 Survey of Social Network Public Opinion Information Extraction Based on Deep Learning 计算机科学, 2022, 49(8): 279-293. https://doi.org/10.11896/jsjkx.220300099 |
[5] | 郝志荣, 陈龙, 黄嘉成. 面向文本分类的类别区分式通用对抗攻击方法 Class Discriminative Universal Adversarial Attack for Text Classification 计算机科学, 2022, 49(8): 323-329. https://doi.org/10.11896/jsjkx.220200077 |
[6] | 姜梦函, 李邵梅, 郑洪浩, 张建朋. 基于改进位置编码的谣言检测模型 Rumor Detection Model Based on Improved Position Embedding 计算机科学, 2022, 49(8): 330-335. https://doi.org/10.11896/jsjkx.210600046 |
[7] | 孙奇, 吉根林, 张杰. 基于非局部注意力生成对抗网络的视频异常事件检测方法 Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection 计算机科学, 2022, 49(8): 172-177. https://doi.org/10.11896/jsjkx.210600061 |
[8] | 胡艳羽, 赵龙, 董祥军. 一种用于癌症分类的两阶段深度特征选择提取算法 Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification 计算机科学, 2022, 49(7): 73-78. https://doi.org/10.11896/jsjkx.210500092 |
[9] | 程成, 降爱莲. 基于多路径特征提取的实时语义分割方法 Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction 计算机科学, 2022, 49(7): 120-126. https://doi.org/10.11896/jsjkx.210500157 |
[10] | 侯钰涛, 阿布都克力木·阿布力孜, 哈里旦木·阿布都克里木. 中文预训练模型研究进展 Advances in Chinese Pre-training Models 计算机科学, 2022, 49(7): 148-163. https://doi.org/10.11896/jsjkx.211200018 |
[11] | 周慧, 施皓晨, 屠要峰, 黄圣君. 基于主动采样的深度鲁棒神经网络学习 Robust Deep Neural Network Learning Based on Active Sampling 计算机科学, 2022, 49(7): 164-169. https://doi.org/10.11896/jsjkx.210600044 |
[12] | 苏丹宁, 曹桂涛, 王燕楠, 王宏, 任赫. 小样本雷达辐射源识别的深度学习方法综述 Survey of Deep Learning for Radar Emitter Identification Based on Small Sample 计算机科学, 2022, 49(7): 226-235. https://doi.org/10.11896/jsjkx.210600138 |
[13] | 祝文韬, 兰先超, 罗唤霖, 岳彬, 汪洋. 改进Faster R-CNN的光学遥感飞机目标检测 Remote Sensing Aircraft Target Detection Based on Improved Faster R-CNN 计算机科学, 2022, 49(6A): 378-383. https://doi.org/10.11896/jsjkx.210300121 |
[14] | 王建明, 陈响育, 杨自忠, 史晨阳, 张宇航, 钱正坤. 不同数据增强方法对模型识别精度的影响 Influence of Different Data Augmentation Methods on Model Recognition Accuracy 计算机科学, 2022, 49(6A): 418-423. https://doi.org/10.11896/jsjkx.210700210 |
[15] | 毛典辉, 黄晖煜, 赵爽. 符合监管合规性的自动合成新闻检测方法研究 Study on Automatic Synthetic News Detection Method Complying with Regulatory Compliance 计算机科学, 2022, 49(6A): 523-530. https://doi.org/10.11896/jsjkx.210300083 |
|