计算机科学 ›› 2021, Vol. 48 ›› Issue (2): 264-270.doi: 10.11896/jsjkx.200300098
吕明琪, 洪照雄, 陈铁明
LYU Ming-qi, HONG Zhao-xiong, CHEN Tie-ming
摘要： 交通流预测作为智能交通系统的一个关键问题,是国内外交通领域的研究热点。交通流预测的主要挑战在于交通流数据本身具有复杂的时空关联,且易受各种社会事件的影响。针对这些挑战,提出一种用于交通流预测的深度学习框架。一方面,针对道路网络非欧氏的空间关联以及交通流时序数据的时间关联,设计了一种融合图卷积神经网络和循环神经网络的特征抽取子网络;另一方面,针对社会事件对交通流的潜在影响,设计了一种基于卷积神经网络的社会事件特征抽取子网络。最后,融合时空关联特征抽取子网络和社会事件特征抽取子网络,实现交通流预测模型。为了验证模型的有效性,文中基于真实交通流数据进行了实验。结果表明,所提模型与传统的预测模型相比具有较高的准确度,准确度提高了3％～6％。
中图分类号:
| [1] YUAN J,ZHENG Y,XIE X,et al.Driving with knowledge from the physical world[C]//Proceeding of KDD.2011:316-324. [2] BELLETTI F,HAZIZA D,GOMES G,et al.Expert level control of ramp metering based on multi-task deep reinforcement learning [J].IEEE Transactions on Intelligent Transportation Systems,2017,99:1-10. [3] ZHAO L,SONG Y J,ZHANG C,et al.Temporal graph convolutional network for urban traffic flow prediction method [J].arXiv:1811.05320v2,2018. [4] XIE P,LI T R,LIU J,et al.Urban flow prediction from spatiotemporal data using machine learning:A survey [J].Information Fusion,2020,59:1-12. [5] ZHAO Z,CHEN W,WU X,et al.LSTM network:A deep lear-ning approach for short-term traffic forecast [J].IET Intelligent Transport Systems,2017,11(2):68-75. [6] KANG D,LYU Y,CHEN Y.Short-term traffic flow prediction with LSTM recurrent neural network [C] ∥Proceeding of ITSC.2017:1-6. [7] DUAN Y,LYU Y,WANG F Y.Travel time prediction with LSTM neural network[C]//Proceeding of ITSC.2016. [8] ZHANG J,ZHENG Y,QI D,et al.Predicting citywide crowd flows using deep spatio-temporal residual networks [J].Artificial Intelligence,2018,259(6):147-166. [9] YU H Y,WU Z H,WANG S Q,et al.Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks [J].Sensors,2017,17(7):1501. [10] YAO H X,WU F,KE J T,et al.Deep multi-view spatial-temporal network for taxi demand prediction [J].arXiv:1802.08714v2,2018. [11] WANG D J,YANG Y,NING S M.DeepSTCL:A Deep Spatiotemporal ConvLSTM for Travel Demand Prediction[C]//Proceeding of IJCNN.2018. [12] ZHANG J B,ZHENG Y,QI D K.Deep spatio-temporal residual networks for citywide crowd flows prediction[C]//Proceeding of AAAI.2016. [13] HAN F,LI C C,ZHAO N,et al.Modeling the impacts of wea-ther,spatial and temporal factors on traffic operation[C]//Proceeding of Advanced Forum on Transportation of China.2012. [14] KEAY K,SIMMONDS I.The association of rainfall and other weather variables with road traffic volume in Melbourne,Australia [J].Accident Analysis & Prevention,2005,37(1):109-124. [15] ZHAO Y,SADEK A W,FUGLEWICZ D.Modeling the impact of inclement weather on freeway traffic speed at macroscopic and microscopic levels [J].Transportation Research Record:Journal of the Transportation Research Board,2012,227(2):173-180. [16] DUNNE S,GHOSH B.Weather adaptive traffic prediction using neurowavelet models [J].IEEE Transactions on Intelligent Transportation Systems,2013,14(1):370-379. [17] KIM Y.Convolutional Neural Networks for Sentence Classification[C]//Proceeding of the 2014 Conference on Empirical Methods on Natural Language Processing.2014:1746-1751. [18] XU X Y,LIU J,LI H Y,et al.Analysis of subway station capa-city with the use of queueing theory [J].Transportation Research Part C:Emerging Technologies,2014,38:28-43. [19] WEI P,CAO Y,SUN D.Total unimodularity and decomposition method for large-scale air traffic cell transmission model [J].Transportation Research Part B:Methodological,2013,53(7):1-16. [20] XU F F,HE Z C,SHA Z R.Impacts of traffic managementmeasures on urban network microscopic fundamental diagram [J].Journal of Transportation Systems Engineering & Information Technology,2013,13(2):185-190. [21] GUO M,XIAO X,LAN J H.Summary of Short-term Forecast Methods for Road Traffic Flow [J].Automation Technology and Application,2009,28(6):8-9,16. [22] 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(1):50-64. [23] KUMAR S V,VANAJAKSHI L.Short-term traffic flow prediction using seasonal ARIMA model with limited input data [J].European Transport Research Review,2015,7. [24] 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 Enginee-ring,2003,129(6):664-672. [25] HU W,YAN L,LIU K,et al.A short-term traffic flow forecasting method based on the hybrid PSO-SVR [J].Neural proces-sing letters,2016,43(1):155-172. [26] CASTILLO E,JOSÉ M,SANTOS S C.Predicting traffic flowusing Bayesian networks [J].Transportation Research Part B:Methodological,2008,42(5):482-509. [27] CAI P L,WANG Y P,LU G Q,et al.A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting [J].Transportation Research Part C:Emerging Technologies,2016,62:21-34. [28] KANOH H,FURUKAWA T,TSUKAHARA S,et al.Short-term traffic prediction using fuzzy C-means and cellular automata in a wide-area road network[C]//Proceeding of IEEE Intelligent Transportation Systems.2005. [29] YANG S,QIAN S.Understanding and predicting roadway travel time with spatio-temporal features of network traffic flow,weather conditions and incidents[C]//Proceeding of Transportation Research Board.2018. [30] MA X,TAO Z,WANG Y,et al.Long short-term memory neural network for traffic speed prediction using remote microwave sensor data [J].Transportation Research Part C:Emerging Technologies,2015,54:187-197. [31] BRUNA J,ZAREMBA W,SZLAM A,et al.Spectral Networks and Locally Connected Networks on Graphs [J].arXiv:1312.6203v3,2014. [32] DEFFERRARD M,BRESSON X,VANDERGHEYNST P.Convolutional neuralnetworks on graphs with fast localized spectral filtering [J].Advancesin Neural Information Processing Systems,2016:3844-3852. [33] KIPF T N,WELLING M.Semi-supervised classification withgraphconvolutional networks[C]//Proceeding of ICLR.2017. [34] LI Y G,YU R,CYRUS S,et al.Diffusion convolutional recurrent neural network:Data-driven traffic forecasting [J].Arxiv.org/abs/1707.01926. [35] ZHANG Z,LI M,LIN X,et al.Multistep speed prediction on traffic networks:A deep learning approach considering spatio-temporal dependencies [J].Transportation Research Part C:Emerging Technologies,2019,105(8):297-322. [36] YAO S C,HU S H,ZHAO Y R,et al.DeepSense:A unifieddeep learning framework for time-series mobile sensing data processing [J].arXiv:1611.01942v2,2017. [37] GUO M,XIAO X,LAN J H.A summary of the short-time traffic flow forecasting methods [J].Techniques of Automation & Applications,2009,28(6):8-9,16. [38] PENNINGTON,JEFFREY,SOCHER,et al.Glove:Global vectors for word representation[C]//Proceeding of the 2014 Conference on Empirical Methods in Natural Language Processing.2014. [39] KE J T,YANG H,ZHENG H Y,et al.Hexagon-based convolutional neural network for supply-demand forecasting of ride-sourcing services [J].IEEE Transactions on Intelligent Transportation Systems,2019,20(11):4160-4173. [40] HAMED M M,AL-MASAEID H R,SAID Z M B.Short-term prediction of traffic volume in urban arterials [J].Journal of Transportation Engineering,1995,121(3):249-254. [41] WU C H,WEI C C,SU D C,et al.Travel time prediction with support vector regression[C]//Proceeding of ITSC.2003. [42] PARK D,RILETT L R.Forecasting freeway link travel times with a multilayer feedforward neural network [J].Computer Aided Civil & Infrastructure Engineering,2002,14(5):357-367. [43] WU Y,TAN H.Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework [J].arXiv:1612.01022v1,2016. |
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