基于深度学习的短时交通量预测研究综述
|
摘要
短时交通量预测是智能交通领域的研究热点,对交通控制与管理具有重要的意义。传统的交通量预测方法难以准确地描述交通量数据内部的本质特征,而深度学习通过其深层结构,能够学习到交通量数据内部复杂的多因素耦合结构,进而对交通量做出更精准的预测,这也使得深度学习成为当前短时交通量预测领域的研究热点。文中首先介绍了传统交通量预测方法和深度学习的研究现状;然后按照生成型和判别型深度结构对现有基于深度学习的短时交通量预测方法进行分类,并总结了深度学习在短时交通量预测研究领域的主要方法,对其性能进行了对比研究;最后对深度学习在短时交通量预测领域存在的问题和发展趋势进行了探讨。
Short-term traffic flow forecasting is a hot topic in the field of intelligent transportation,which is of great significance in traffic control and management.The traditional traffic flow forecasting methods are difficult to describe the internal characteristics of the traffic data accurately.Deep learning can learn the internal complex multivariate coupled structure of the traffic flow data through its deep structure and then make a more accurate forecasting of the traffic flow,which makes deep learning a hot topic in the current traffic flow forecasting field.Firstly,the traditional traffic flow forecasting methods and the current research status of deep learning were briefly introduced.Then the methods of short-term traffic flow forecasting based on deep learning were classified according to generative deep architecture and discriminative deep architecture.This paper also summarized the main methods of deep learning in the field of traffic flow forecasting and compared their performance.Finally,the existing problems and development directions of deep learning in short-term traffic flow forecasting were discussed.
Short-term traffic flow forecasting is a hot topic in the field of intelligent transportation,which is of great significance in traffic control and management.The traditional traffic flow forecasting methods are difficult to describe the internal characteristics of the traffic data accurately.Deep learning can learn the internal complex multivariate coupled structure of the traffic flow data through its deep structure and then make a more accurate forecasting of the traffic flow,which makes deep learning a hot topic in the current traffic flow forecasting field.Firstly,the traditional traffic flow forecasting methods and the current research status of deep learning were briefly introduced.Then the methods of short-term traffic flow forecasting based on deep learning were classified according to generative deep architecture and discriminative deep architecture.This paper also summarized the main methods of deep learning in the field of traffic flow forecasting and compared their performance.Finally,the existing problems and development directions of deep learning in short-term traffic flow forecasting were discussed.
引文
[1] HINTON G E,OSINDERO S,TEH Y W.A Fast Learning Algorithm for Deep Belief Nets[J].Neural Computation,2006,18(7):1527-1554.
[2] 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.
[3] LEVIN M,TSAO Y D.On Forecasting Freeway Occupancies and Volumes[J].Transportation Research Record,1980(722):47-49.
[4] WILLIAMS B M,HOEL L A.Modeling and Forecasting Vehicu- lar Traffic Flow as a Seasonal ARIMA Process:Theoretical Basis and Empirical Results[J].Journal of Transportation Engineering,2003,129(6):664-672.
[5] LEE S,FAMBRO D.Application of Subset Autoregressive Integrated Moving Average Model for Short-Term Freeway Traffic Volume Forecasting[J].Transportation Research Record Journal of the Transportation Research Board,1999,1678(1):179-188.
[6] OKUTANI I,STEPHANEDES Y J.Dynamic Prediction of Traffic Volume through Kalman Filtering Theory[J].Transportation Research Part B,1984,18(1):1-11.
[7] 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,2014,43:50-64.
[8] ZHANG Y,XIE Y.Forecasting of Short-Term Freeway Volume with v Support Vector Machines[J].Transportation Research Record Journal of the Transportation Research Board,2007,2024(1):92-99.
[9] DAVIS G A,NIHAN N L.Nonparametric Regression and Short-Term Freeway Traffic Forecasting[J].Journal of Transportation Engineering,1991,117(2):178-188.
[10] SMITH B L,DEMETSKY M J.Traffic Flow Forecasting:Comparison of Modeling Approaches[J].Journal of Transportation Engineering,1997,123(4):261-266.
[11] WU C H,HO J M,LEE D T.Travel-Time Prediction with Support Vector Regression[J].IEEE Transactions on Intelligent Transportation Systems,2004,5(4):276-281.
[12] XIE Y,ZHAO K,SUN Y,et al.Gaussian Processes for Short- Term Traffic Volume Forecasting[J].Transportation Research Record Journal of the Transportation Research Board,2010,2165:69-78.
[13] ZHANG L,LIU Q,YANG W,et al.An Improved K-Nearest Neighbor Model for Short-Term Traffic Flow Prediction[J].Procedia-Social and Behavioral Sciences,2013,96:653-662.
[14] ZHENG Z,SU D.Short-Term Traffic Volume Forecasting:A K-Nearest Neighbor Approach Enhanced by Constrained LinearlySewing Principle Component Algorithm[J].Transportation Research Part C Emerging Technologies,2014,43:143-157.
[15] WANG J,DENG W,ZHAO J B.Short-Term Freeway Traffic Flow Prediction Based on Improved Bayesian Combined Model[J].Journal of Southeast University (Natural Science Edition),2012,42(1):162-167.(in Chinese)王建,邓卫,赵金宝.基于改进型贝叶斯组合模型的短时交通流量预测[J].东南大学学报(自然科学版),2012,42(1):162-167.
[16] SMITH B L,DEMETSKY M J.Short-Term Traffic Flow Prediction:Neural Network Approach[J].Transportation Research Record,1994(1453):98-104.
[17] MESSER C,THOMAS URBANIK I I.Short-Term Freeway Traffic Volume Forecasting Using Radial Basis Function Neural Network[J].Transportation Research Record Journal of the Transportation Research Board,1998(1651):39-47.
[18] BENGIO Y,LECUN Y.Scaling Learning Algorithms Towards AI[J].Large-Scale Kernel Machines,2007,34(5):1-41.
[19] ERFANI S M,RAJASEGARAR S,KARUNASEKERA S,et al.High-Dimensional and Large-Scale Anomaly Detection Using a Linear One-Class SVM with Deep Learning[J].Pattern Re-cognition,2016,58(C):121-134.
[20] LECUN Y,BENGIO Y,HINTON G.Deep Learning[J].Nature,2015,521(7553):436-444.
[21] BENGIO Y.Learning Deep Architectures for AI[J].Founda- tions & Trends in Machine Learning,2009,2(1):1-127.
[22] LI D,DONG Y.Deep Learning:Methods and Applications[J].Foundations & Trends? in Signal Processing,2014,7(3):197-387.
[23] SUN Z Y,LU C X,SHI Z Z,et al.Research and Advances on Deep Learning[J].Computer Science,2016,43(2):1-8.(in Chinese)孙志远,鲁成祥,史忠植,等.深度学习研究与进展[J].计算机科学,2016,43(2):1-8.
[24] JIA J P,QIN Y H.Survey on Visual Tracking Algorithms Based on Deep Learning Tehnologies[J].Computer Science,2017,44(s1):19-23.(in Chinese)贾静平,覃亦华.基于深度学习的视觉跟踪算法研究综述[J].计算机科学,2017,44(s1):19-23.
[25] ROUX N L,BENGIO Y.Representational Power of Restricted Boltzmann Machines and Deep Belief Networks[J].Neural Computation,2008,20(6):1631-1649.
[26] HUANG W,SONG G,HONG H,et al.Deep Architecture for Traffic Flow Prediction:Deep Belief Networks with Multitask Learning[J].IEEE Transactions on Intelligent Transportation Systems,2014,15(5):2191-2201.
[27] CARUANA R.Multitask Learning[J].Machine Learning,1997,28(1):41-75.
[28] SOUA R,KOESDWIADY A,KARRAY F.Big-Data-Generated Traffic Flow Prediction Using Deep Learning and Dempster-Shafer Theory[C]//International Joint Conference on Neural Networks.2016:3195-3202.
[29] DEMPSTER A P.The Dempster-Shafer Calculus for Statisti- cians[J].International Journal of Approximate Reasoning,2007,48(2):365-377.
[30] KOESDWIADY A,SOUA R,KARRAY F.Improving Traffic Flow Prediction with Weather Information in Connected Cars:A Deep Learning Approach[J].IEEE Transactions on Vehicular Technology,2016,65(12):9508-9517.
[31] 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):91-93.(in Chinese)罗向龙,焦琴琴,牛力瑶,等.基于深度学习的短时交通流预测[J].计算机应用研究,2017,34(1):91-93.
[32] TAN H,XUAN X,WU Y,et al.A Comparison of Traffic Flow Prediction Methods Based on DBN[C]//Cota International Conference of Transportation Professionals.Shanghai,China,2016:273-283.
[33] HINTON G E.A Practical Guide to Training Restricted Boltzmann Machines[J].Momentum,2012,9(1):599-619.
[34] HINTON G E,SALAKHUTDINOV R.Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes[C]//International Conference on Neural Information Processing Systems.2008:1249-1256.
[35] HINTON G E,SALAKHUTDINOV R.Reducing the Dimen- sionality of Data with Neural Networks[J].Science,2006,313(5786):504-507.
[36] PALM R B.Prediction as a Candidate for Learning Deep Hierar- chical Models of Data[D].Kongens Lyngby :Technical University of Denmark,2012.
[37] LV Y,DUAN Y,KANG W,et al.Traffic Flow Prediction with Big Data:A Deep Learning Approach[J].IEEE Transactions on Intelligent Transportation Systems,2015,16(2):865-873.
[38] DUAN Y,LV Y,WANG F.Performance Evaluation of the Deep Learning Approach for Traffic Flow Prediction at Different Times[C]//International conference on service operations and logistics,and informatics.IEEE,2016:223-227.
[39] YANG H F,DILLON T S,CHEN Y P.Optimized Structure of the Traffic Flow Forecasting Model with a Deep Learning Approach[J].IEEE transactions on neural networks and learning systems,2017,28(10):2371-2381.
[40] ZHOU T,HAN G,XU X,et al.δ -Agree Adaboost Stacked Autoencoder for Short-Term Traffic Flow Forecasting[J].Neurocomputing,2017,247:31-38.
[41] TANG J,XU G,WANG Y,et al.Traffic Flow Prediction Based on Hybrid Model Using Double Exponential Smoothing and Support Vector Machine[C]//International IEEE Conference on Intelligent Transportation Systems.2014:130-135.
[42] KANZOW C,YAMASHITA N,FUKUSHIMA M.Levenberg-Marquardt Methods with Strong Local Convergence Properties for Solving Nonlinear Equations with Convex Constraints[M].Elsevier Science Publishers B V,2004:321-343.
[43] TSAI J T,CHANG C C,CHEN W P,et al.Optimal Parameter Design for Ic Wire Bonding Process by Using Fuzzy Logic and Taguchi Method[J].IEEE Access,2017,4:3034-3045.
[44] KAWAGUCHI K.Deep Learning without Poor Local Minima[C]//the 30th Conference on Neural Information Processing Systems.Barcelona,Spain,2016:586-594.
[45] SCHAPIRE R E,FREUND Y.Boosting:Foundations and Algorithms[J].Kybernetes,2013,42(1):164-166.
[46] PINEDA F J.Generalization of Back-Propagation to Recurrent Neural Networks[J].Physical Review Letters,1987,59(19):2229-2232.
[47] SUTSKEVER I,MARTENS J,HINTON G E.Generating Text with Recurrent Neural Networks[C]//International Conference on Machine Learning.Bellevue,Washington,USA,2011:1017-1024.
[48] ELMAN J L.Finding Structure in Time[J].Cognitive Science,1990,14(2):179-211.
[49] VAN LINT J,HOOGENDOORN S,VAN ZUYLEN H.Free- way Travel Time Prediction with State-Space Neural Networks:Modeling State-Space Dynamics with Recurrent Neural Networks[J].Transportation Research Record:Journal of the Transportation Research Board,2002(1811):30-39.
[50] BENGIO Y,SIMARD P,FRASCONI P.Learning Long-Term Dependencies with Gradient Descent Is Difficult[J].IEEE Transactions on Neural Networks,1994,5(2):157-166.
[51] PASCANU R,MIKOLOV T,BENGIO Y.On the Difficulty of Training Recurrent Neural Networks[C]//International Conference on Machine Learning.Atlanta,GA,USA,2013:1310-1318.
[52] HOCHREITER S,SCHMIDHUBER J.Long Short-Term Me- mory[J].Neural Computation,1997,9(8):1735-1780.
[53] LE Q V,JAITLY N,HINTON G E.A Simple Way to Initialize Recurrent Networks of Rectified Linear Units[J].arXiv:1504.00941,2015.
[54] ZHAO Z,CHEN W,WU X,et al.LSTM Network:A Deep Learning Approach for Short-Term Traffic Forecast[J].IET Intelligent Transport Systems,2017,11(2):68-75.
[55] JIA Y,WU J,XU M.Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method[J].Journal of Advanced Transportation,2017,2017(722):1-10.
[56] FU R,ZHANG Z,LI L.Using LSTM and GRU Neural Net- work Methods for Traffic Flow Prediction[C]//Youth Acade-mic Annual Conference of Chinese Association of Automation (YAC).Wuhan,China,2017:324-328.
[57] TIAN Y,PAN L.Predicting Short-Term Traffic Flow by Long Short-Term Memory Recurrent Neural Network[C]//IEEE International Conference on Smart City.Chengdu,China,2015:153-158.
[58] CHEN Y Y,LV Y S,LI Z J,et al.Long Short-Term Memory Model for Traffic Congestion Prediction with Online Open Data[C]//IEEE International Conference on Intelligent Transportation Systems.Rio de Janeiro,Brazil,2016:132-137.
[59] SHAO H,SOONG B H.Traffic Flow Prediction with Long Short-Term Memory Networks (LSTMs)[C]//Region 10 Conference(TENCON).IEEE,2017:2986-2989.
[60] XUE W X,XU L H.Short-Term Traffic Flow Prediction Based on Deep Learning[J].Journal of Transportation Systems Engineering and Information Technology,2018,18(1):81-88.(in Chinese)王祥雪,许伦辉.基于深度学习的短时交通流预测研究[J].交通运输系统工程与信息,2018,18(1):81-88.
[61] CHUNG E.Does Weather Affect Highway Capacity[C]∥5th International Symposium on Highway Capacity and Quality of Service.Yakoma,Japan,2006.
[62] MAZE T H,AGARWAI M,BURCHETT G.Whether Weather Matters to Traffic Demand,Traffic Safety,and Traffic Operations and Flow[J].Transportation Research Record Journal of the Transportation Research Board,2006,1948(1):170-176.
[63] TANG Y,HUANG Y,WU Z,et al.Question Detection from Acoustic Features Using Recurrent Neural Network with Gated Recurrent Unit[C]//IEEE International Conference on Acoustics,Speech and Signal Processing.IEEE,2016:6125-6129.
[64] RAWAT W,WANG Z.Deep Convolutional Neural Networks for Image Classification:A Comprehensive Review[J].Neural Computation,2017,29(9):2352-2449.
[65] GOODFELLOW I,BENGIO Y,COURVILLE A.Deep Learning[M].Cambridge,Massachusetts,USA:MIT Press,2016.
[66] LUO W H,DONG B T,WANG Z S.Short-Term Traffic Flow Prediction Based on CNN-SVR Hybrid Deep Learning Model[J].Journal of Transportation Systems Engineering and Information Technology,2017,17(5):68-74.(in Chinese)罗文慧,董宝田,王泽胜.基于CNN-SVR混合深度学习模型的短时交通流预测[J].交通运输系统工程与信息,2017,17(5):68-74.
[67] WU Y K,TAN H C,QIN L Q,et al.A Hybrid Deep Learning Based Traffic Flow Prediction Method and Its Understanding[J].Transportation Research Part C:Emerging Technologies,2018,90:166-180.
[68] DU S,LI T,GONG X,et al.A Hybrid Method for Traffic Flow Forecasting Using Multimodal Deep Learning[J].arXiv:1803.02099,2018.
[69] NGIAM J,KHOSLA A,KIM M,et al.Multimodal Deep Lear- ning[C]//International Conference on Machine Learning.Bellevue,Washington,USA,2011:689-696.
[70] SHANAHAN J,LIANG D.Large Scale Distributed Data Science from Scratch Using Apache Spark 2.0[C]//Internatio-nal Conference on World Wide Web Companion.New York:ACM,2017:955-957.
[71] ALSHEIKH M A,NIYATO D,LIN S,et al.Mobile Big Data Analytics Using Deep Learning and Apache Spark[J].IEEE Network,2016,30(3):22-29.
[72] COELHO I M,COELHO V N,LUZ E J D S,et al.A GPU Deep Learning Metaheuristic Based Model for Time Series Forecasting[J].Applied Energy,2017,201:412-418.
[73] WANG C,GONG L,YU Q,et al.Dlau:A Scalable Deep Lear- ning Accelerator Unit on FPGA[J].IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems,2017,36(3):513-517.
[74] VEERIAH V,ZHUANG N,QI G J.Differential Recurrent Neural Networks for Action Recognition[C]//IEEE International Conference on Computer Vision.IEEE,2015:4041-4049.
[75] GRAVES A,FERNáNDEZ S,SCHMIDHUBER J.Multi-Di- mensional Recurrent Neural Networks[C]//International Conference on Artificial Neural Networks.Berlin:Springer,2007:549-558.
[2] 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.
[3] LEVIN M,TSAO Y D.On Forecasting Freeway Occupancies and Volumes[J].Transportation Research Record,1980(722):47-49.
[4] WILLIAMS B M,HOEL L A.Modeling and Forecasting Vehicu- lar Traffic Flow as a Seasonal ARIMA Process:Theoretical Basis and Empirical Results[J].Journal of Transportation Engineering,2003,129(6):664-672.
[5] LEE S,FAMBRO D.Application of Subset Autoregressive Integrated Moving Average Model for Short-Term Freeway Traffic Volume Forecasting[J].Transportation Research Record Journal of the Transportation Research Board,1999,1678(1):179-188.
[6] OKUTANI I,STEPHANEDES Y J.Dynamic Prediction of Traffic Volume through Kalman Filtering Theory[J].Transportation Research Part B,1984,18(1):1-11.
[7] 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,2014,43:50-64.
[8] ZHANG Y,XIE Y.Forecasting of Short-Term Freeway Volume with v Support Vector Machines[J].Transportation Research Record Journal of the Transportation Research Board,2007,2024(1):92-99.
[9] DAVIS G A,NIHAN N L.Nonparametric Regression and Short-Term Freeway Traffic Forecasting[J].Journal of Transportation Engineering,1991,117(2):178-188.
[10] SMITH B L,DEMETSKY M J.Traffic Flow Forecasting:Comparison of Modeling Approaches[J].Journal of Transportation Engineering,1997,123(4):261-266.
[11] WU C H,HO J M,LEE D T.Travel-Time Prediction with Support Vector Regression[J].IEEE Transactions on Intelligent Transportation Systems,2004,5(4):276-281.
[12] XIE Y,ZHAO K,SUN Y,et al.Gaussian Processes for Short- Term Traffic Volume Forecasting[J].Transportation Research Record Journal of the Transportation Research Board,2010,2165:69-78.
[13] ZHANG L,LIU Q,YANG W,et al.An Improved K-Nearest Neighbor Model for Short-Term Traffic Flow Prediction[J].Procedia-Social and Behavioral Sciences,2013,96:653-662.
[14] ZHENG Z,SU D.Short-Term Traffic Volume Forecasting:A K-Nearest Neighbor Approach Enhanced by Constrained LinearlySewing Principle Component Algorithm[J].Transportation Research Part C Emerging Technologies,2014,43:143-157.
[15] WANG J,DENG W,ZHAO J B.Short-Term Freeway Traffic Flow Prediction Based on Improved Bayesian Combined Model[J].Journal of Southeast University (Natural Science Edition),2012,42(1):162-167.(in Chinese)王建,邓卫,赵金宝.基于改进型贝叶斯组合模型的短时交通流量预测[J].东南大学学报(自然科学版),2012,42(1):162-167.
[16] SMITH B L,DEMETSKY M J.Short-Term Traffic Flow Prediction:Neural Network Approach[J].Transportation Research Record,1994(1453):98-104.
[17] MESSER C,THOMAS URBANIK I I.Short-Term Freeway Traffic Volume Forecasting Using Radial Basis Function Neural Network[J].Transportation Research Record Journal of the Transportation Research Board,1998(1651):39-47.
[18] BENGIO Y,LECUN Y.Scaling Learning Algorithms Towards AI[J].Large-Scale Kernel Machines,2007,34(5):1-41.
[19] ERFANI S M,RAJASEGARAR S,KARUNASEKERA S,et al.High-Dimensional and Large-Scale Anomaly Detection Using a Linear One-Class SVM with Deep Learning[J].Pattern Re-cognition,2016,58(C):121-134.
[20] LECUN Y,BENGIO Y,HINTON G.Deep Learning[J].Nature,2015,521(7553):436-444.
[21] BENGIO Y.Learning Deep Architectures for AI[J].Founda- tions & Trends in Machine Learning,2009,2(1):1-127.
[22] LI D,DONG Y.Deep Learning:Methods and Applications[J].Foundations & Trends? in Signal Processing,2014,7(3):197-387.
[23] SUN Z Y,LU C X,SHI Z Z,et al.Research and Advances on Deep Learning[J].Computer Science,2016,43(2):1-8.(in Chinese)孙志远,鲁成祥,史忠植,等.深度学习研究与进展[J].计算机科学,2016,43(2):1-8.
[24] JIA J P,QIN Y H.Survey on Visual Tracking Algorithms Based on Deep Learning Tehnologies[J].Computer Science,2017,44(s1):19-23.(in Chinese)贾静平,覃亦华.基于深度学习的视觉跟踪算法研究综述[J].计算机科学,2017,44(s1):19-23.
[25] ROUX N L,BENGIO Y.Representational Power of Restricted Boltzmann Machines and Deep Belief Networks[J].Neural Computation,2008,20(6):1631-1649.
[26] HUANG W,SONG G,HONG H,et al.Deep Architecture for Traffic Flow Prediction:Deep Belief Networks with Multitask Learning[J].IEEE Transactions on Intelligent Transportation Systems,2014,15(5):2191-2201.
[27] CARUANA R.Multitask Learning[J].Machine Learning,1997,28(1):41-75.
[28] SOUA R,KOESDWIADY A,KARRAY F.Big-Data-Generated Traffic Flow Prediction Using Deep Learning and Dempster-Shafer Theory[C]//International Joint Conference on Neural Networks.2016:3195-3202.
[29] DEMPSTER A P.The Dempster-Shafer Calculus for Statisti- cians[J].International Journal of Approximate Reasoning,2007,48(2):365-377.
[30] KOESDWIADY A,SOUA R,KARRAY F.Improving Traffic Flow Prediction with Weather Information in Connected Cars:A Deep Learning Approach[J].IEEE Transactions on Vehicular Technology,2016,65(12):9508-9517.
[31] 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):91-93.(in Chinese)罗向龙,焦琴琴,牛力瑶,等.基于深度学习的短时交通流预测[J].计算机应用研究,2017,34(1):91-93.
[32] TAN H,XUAN X,WU Y,et al.A Comparison of Traffic Flow Prediction Methods Based on DBN[C]//Cota International Conference of Transportation Professionals.Shanghai,China,2016:273-283.
[33] HINTON G E.A Practical Guide to Training Restricted Boltzmann Machines[J].Momentum,2012,9(1):599-619.
[34] HINTON G E,SALAKHUTDINOV R.Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes[C]//International Conference on Neural Information Processing Systems.2008:1249-1256.
[35] HINTON G E,SALAKHUTDINOV R.Reducing the Dimen- sionality of Data with Neural Networks[J].Science,2006,313(5786):504-507.
[36] PALM R B.Prediction as a Candidate for Learning Deep Hierar- chical Models of Data[D].Kongens Lyngby :Technical University of Denmark,2012.
[37] LV Y,DUAN Y,KANG W,et al.Traffic Flow Prediction with Big Data:A Deep Learning Approach[J].IEEE Transactions on Intelligent Transportation Systems,2015,16(2):865-873.
[38] DUAN Y,LV Y,WANG F.Performance Evaluation of the Deep Learning Approach for Traffic Flow Prediction at Different Times[C]//International conference on service operations and logistics,and informatics.IEEE,2016:223-227.
[39] YANG H F,DILLON T S,CHEN Y P.Optimized Structure of the Traffic Flow Forecasting Model with a Deep Learning Approach[J].IEEE transactions on neural networks and learning systems,2017,28(10):2371-2381.
[40] ZHOU T,HAN G,XU X,et al.δ -Agree Adaboost Stacked Autoencoder for Short-Term Traffic Flow Forecasting[J].Neurocomputing,2017,247:31-38.
[41] TANG J,XU G,WANG Y,et al.Traffic Flow Prediction Based on Hybrid Model Using Double Exponential Smoothing and Support Vector Machine[C]//International IEEE Conference on Intelligent Transportation Systems.2014:130-135.
[42] KANZOW C,YAMASHITA N,FUKUSHIMA M.Levenberg-Marquardt Methods with Strong Local Convergence Properties for Solving Nonlinear Equations with Convex Constraints[M].Elsevier Science Publishers B V,2004:321-343.
[43] TSAI J T,CHANG C C,CHEN W P,et al.Optimal Parameter Design for Ic Wire Bonding Process by Using Fuzzy Logic and Taguchi Method[J].IEEE Access,2017,4:3034-3045.
[44] KAWAGUCHI K.Deep Learning without Poor Local Minima[C]//the 30th Conference on Neural Information Processing Systems.Barcelona,Spain,2016:586-594.
[45] SCHAPIRE R E,FREUND Y.Boosting:Foundations and Algorithms[J].Kybernetes,2013,42(1):164-166.
[46] PINEDA F J.Generalization of Back-Propagation to Recurrent Neural Networks[J].Physical Review Letters,1987,59(19):2229-2232.
[47] SUTSKEVER I,MARTENS J,HINTON G E.Generating Text with Recurrent Neural Networks[C]//International Conference on Machine Learning.Bellevue,Washington,USA,2011:1017-1024.
[48] ELMAN J L.Finding Structure in Time[J].Cognitive Science,1990,14(2):179-211.
[49] VAN LINT J,HOOGENDOORN S,VAN ZUYLEN H.Free- way Travel Time Prediction with State-Space Neural Networks:Modeling State-Space Dynamics with Recurrent Neural Networks[J].Transportation Research Record:Journal of the Transportation Research Board,2002(1811):30-39.
[50] BENGIO Y,SIMARD P,FRASCONI P.Learning Long-Term Dependencies with Gradient Descent Is Difficult[J].IEEE Transactions on Neural Networks,1994,5(2):157-166.
[51] PASCANU R,MIKOLOV T,BENGIO Y.On the Difficulty of Training Recurrent Neural Networks[C]//International Conference on Machine Learning.Atlanta,GA,USA,2013:1310-1318.
[52] HOCHREITER S,SCHMIDHUBER J.Long Short-Term Me- mory[J].Neural Computation,1997,9(8):1735-1780.
[53] LE Q V,JAITLY N,HINTON G E.A Simple Way to Initialize Recurrent Networks of Rectified Linear Units[J].arXiv:1504.00941,2015.
[54] ZHAO Z,CHEN W,WU X,et al.LSTM Network:A Deep Learning Approach for Short-Term Traffic Forecast[J].IET Intelligent Transport Systems,2017,11(2):68-75.
[55] JIA Y,WU J,XU M.Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method[J].Journal of Advanced Transportation,2017,2017(722):1-10.
[56] FU R,ZHANG Z,LI L.Using LSTM and GRU Neural Net- work Methods for Traffic Flow Prediction[C]//Youth Acade-mic Annual Conference of Chinese Association of Automation (YAC).Wuhan,China,2017:324-328.
[57] TIAN Y,PAN L.Predicting Short-Term Traffic Flow by Long Short-Term Memory Recurrent Neural Network[C]//IEEE International Conference on Smart City.Chengdu,China,2015:153-158.
[58] CHEN Y Y,LV Y S,LI Z J,et al.Long Short-Term Memory Model for Traffic Congestion Prediction with Online Open Data[C]//IEEE International Conference on Intelligent Transportation Systems.Rio de Janeiro,Brazil,2016:132-137.
[59] SHAO H,SOONG B H.Traffic Flow Prediction with Long Short-Term Memory Networks (LSTMs)[C]//Region 10 Conference(TENCON).IEEE,2017:2986-2989.
[60] XUE W X,XU L H.Short-Term Traffic Flow Prediction Based on Deep Learning[J].Journal of Transportation Systems Engineering and Information Technology,2018,18(1):81-88.(in Chinese)王祥雪,许伦辉.基于深度学习的短时交通流预测研究[J].交通运输系统工程与信息,2018,18(1):81-88.
[61] CHUNG E.Does Weather Affect Highway Capacity[C]∥5th International Symposium on Highway Capacity and Quality of Service.Yakoma,Japan,2006.
[62] MAZE T H,AGARWAI M,BURCHETT G.Whether Weather Matters to Traffic Demand,Traffic Safety,and Traffic Operations and Flow[J].Transportation Research Record Journal of the Transportation Research Board,2006,1948(1):170-176.
[63] TANG Y,HUANG Y,WU Z,et al.Question Detection from Acoustic Features Using Recurrent Neural Network with Gated Recurrent Unit[C]//IEEE International Conference on Acoustics,Speech and Signal Processing.IEEE,2016:6125-6129.
[64] RAWAT W,WANG Z.Deep Convolutional Neural Networks for Image Classification:A Comprehensive Review[J].Neural Computation,2017,29(9):2352-2449.
[65] GOODFELLOW I,BENGIO Y,COURVILLE A.Deep Learning[M].Cambridge,Massachusetts,USA:MIT Press,2016.
[66] LUO W H,DONG B T,WANG Z S.Short-Term Traffic Flow Prediction Based on CNN-SVR Hybrid Deep Learning Model[J].Journal of Transportation Systems Engineering and Information Technology,2017,17(5):68-74.(in Chinese)罗文慧,董宝田,王泽胜.基于CNN-SVR混合深度学习模型的短时交通流预测[J].交通运输系统工程与信息,2017,17(5):68-74.
[67] WU Y K,TAN H C,QIN L Q,et al.A Hybrid Deep Learning Based Traffic Flow Prediction Method and Its Understanding[J].Transportation Research Part C:Emerging Technologies,2018,90:166-180.
[68] DU S,LI T,GONG X,et al.A Hybrid Method for Traffic Flow Forecasting Using Multimodal Deep Learning[J].arXiv:1803.02099,2018.
[69] NGIAM J,KHOSLA A,KIM M,et al.Multimodal Deep Lear- ning[C]//International Conference on Machine Learning.Bellevue,Washington,USA,2011:689-696.
[70] SHANAHAN J,LIANG D.Large Scale Distributed Data Science from Scratch Using Apache Spark 2.0[C]//Internatio-nal Conference on World Wide Web Companion.New York:ACM,2017:955-957.
[71] ALSHEIKH M A,NIYATO D,LIN S,et al.Mobile Big Data Analytics Using Deep Learning and Apache Spark[J].IEEE Network,2016,30(3):22-29.
[72] COELHO I M,COELHO V N,LUZ E J D S,et al.A GPU Deep Learning Metaheuristic Based Model for Time Series Forecasting[J].Applied Energy,2017,201:412-418.
[73] WANG C,GONG L,YU Q,et al.Dlau:A Scalable Deep Lear- ning Accelerator Unit on FPGA[J].IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems,2017,36(3):513-517.
[74] VEERIAH V,ZHUANG N,QI G J.Differential Recurrent Neural Networks for Action Recognition[C]//IEEE International Conference on Computer Vision.IEEE,2015:4041-4049.
[75] GRAVES A,FERNáNDEZ S,SCHMIDHUBER J.Multi-Di- mensional Recurrent Neural Networks[C]//International Conference on Artificial Neural Networks.Berlin:Springer,2007:549-558.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |