Computer Science

Computer Science ›› 2019, Vol. 46 ›› Issue (5): 175-184.doi: 10.11896/j.issn.1002-137X.2019.05.027

Previous Articles     Next Articles

Short-term Bus Passenger Flow Prediction Based on Improved Convolutional Neural Network

CHEN Shen-jin, XUE Yang   

  1. (School of Electronic and Information Engineering,South China University of Technology,Guangzhou 510641,China)
  • Received:2019-01-18 Revised:2019-03-17 Published:2019-05-15

PDF (PC)

1522

Abstract: Aiming at the random,time-varying and uncertain problems of urban public transport passenger flow,this paper proposed an unsupervised feature learning theory and an improved convolutional neural network based short-term bus station passenger flow prediction,which provides real-time,accurate and effective bus travel services for citizens.In order to prevent and reduce the occurrence of over-fitting,an efficient and reliable model prediction system based on DropSample training method of improved convolutional neural network is constructed.During the training process,the model can be used to describe the short-term passenger flow in different dates and different time periods.The optimizer of Adam algorithm is used to optimize the model,the network model parameters are updated,and different parameters for the adaptive learning rate are set.The results show that the root mean square error of the improved CNN network model is 229.539 and the average absolute percentage error is 0.117.Compared with CNN network model,multiple li-near regression model,Kalman filter model and BP neural network model,this model is more accurate and reliable.The prediction error of the proposed method is smaller,and an example proves that the improved model and algorithm are practical and reliable.

Key words: Bus passenger flow, Convolutional neural network, DropSample training method, Model prediction system, Unsupervised learning

CLC Number: 

  • TP181
[1]BAI Y,SUN Z,ZENG B,et al.A multi-pattern deep fusion modelfor short-term bus passenger flow forecasting[J].Applied Soft Computing,2017,58(1):669-680.
[2]XUE R,JIAN,CHEN S K.Short-term bus passerger demandprediction based on time series model and interactive multiple model approach[J].Discrete Dynamics in Nature and Society,2015,66(1):61-78.
[3]LI X S,WANG S L.Estimation of parameters of linear regression model with linear constraints [J].Statistical Research,2016,33(11):86-92.(in Chinese)李小胜,王申令.带线性约束的多元线性回归模型参数估计[J].统计研究,2016,33(11):86-92.
[4]RASYIDI M A,KIM J,RYU K R.Short-Term Prediction of Vehicle Speed on Main City Roads using the k-Nearest Neighbor Algorithm [J].Journal of Intelligence and Information Systems (S0925-9902),2014,20(1):121-131.
[5]ZHANG C H,SONG R,SUN Y.Kalman Filter-Based ShortTerm Passenger Flow Forecasting on Bus Stop[J].Journal of Transportation Systems Engineering and Information Technology,2011,11(4):154-159.(in Chinese)张春辉,宋瑞,孙杨.基于卡尔曼滤波的公交站点短时客流预测[J].交通运输系统工程与信息,2011,11(4):154-159.
[6]YANG X F,LIU L F.Short-term passenger flow prediction at bus stops based on Support Vector Machine Based on AP clustering [J].Journal of Wuhan University of Technology (Transportation Science and Engineering Edition),2016,40(1):36-40.(in Chinese)杨信丰,刘兰芬.基于AP聚类的支持向量机公交站点短时客流预测[J].武汉理工大学学报(交通科学与工程版),2016,40(1):36-40.
[7]COSTARELLI D,VINTI G.Pointwise and uniform approximation by multivariate neural network operators of the max-pro-duct type[J].Neural Networks,2016,81(9):81-90.
[8]VIEIRA S,PINAYA W H L,MECHELLI A.Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders:methods and applications [J].Neuroscience and Biobehavioral Reviews,2017,74:58-75.
[9]焦李成,赵进,杨淑媛,等.深度学习、优化与识别[M].北京:清华大学出版社,2017:100-120.
[10]LEE H,GROSSE R,RANGANATH R,et al.Unsupervisedlearning of hierarchical representations with convolutional deep belief networks[J].Communications of the ACM,2011,54(10):95-103.
[11]HE Y Q,LI B Q.A Combinatorial Learning Model Learning Rate Strategy [J].Journal of Automation,2016,42(6):954-958.(in Chinese)贺昱曜,李宝奇.一种组合型的深度学习模型学习率策略[J].自动化学报,2016,42(6):954-958.
[12]WANG X X,XU L H.Research on Short-term Traffic FlowForecasting Based on Deep Learning [J].Transportation System Engineering and Information,2018,18(1):82-87.(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):82-87.
[13]DENG L,YU D.Deep learning:methods and applications [J].Foundations and Trends in Signal Processing,2014,7(4):197-387.
[14]AREL I,ROSE D C,KARNOWSKI T P.Deep machine learning a new frontier in artificial intelligence research[J].IEEE Computational Intelligence Magazine,2010,5(4):13-18.
[15]DONG H Z,LIU Q,FU F J.A string invariant prediction method for short-term bus passenger flow[J].Pattern Recognition and Artificial Intelligence,2018,31(9):846-855.(in Chinese)董红召,刘倩,付凤杰.短时公交客流的弦不变量预测方法[J].模式识别与人工智能,2018,31(9):846-855.
[16]FENG W,XIE L,ZENG J,et al.Audio-visual human recognition using semi-supervised spectral learning and hiddenMarkov mo-dels[J].Journal of Visual Languages and Computing,2009,20(3):188-195.
[17]HINTON G E,OSINDERO S,TEH Y W.A fast learning algorithm for deep belief nets[J].Neural Computation,2006,18(7):1527-1554.
[18]SABOKROU M,FATHY M,HOSEINI M.Video anomaly detection and localisation based on the sparsity and reconstruction error of auto-encoder[J].Electronics Letters,2016,52(13):1122-1124.
[19]DAHL G E,YU D,DENG L,et al.Context-dependent pre-trained deep neural networks for large vocabulary speech recognition[J].IEEE Transactions on Audio,Speech,and Language Processing,2012,20(1):30-42.
[20]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.ImageNet classification with deep convolutional neural networks[C]∥Proceedings of the 26th Annual Conference on Neural Information Processing Systems 2012.South Lake Tahoe,NV,USA,2012:1097-1105.
[21]SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[J].arXiv:1409.1556,2014.
[22]HE K M,ZHANG X Y,REN S Q,et al.Deep residual learning for image recognition[C]∥Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas,NV,USA,2016:770-778.
[23]SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[J].arXiv:1409.1556v6,2014.
[24]CHEN L C,PAPANDREOU G,KOKKINOS I,et al.Semantic image segmentation with deep convolutional nets and fully connected CRFs[J].Computer Science,2016,26(4):357-361.
[25]HINTON G E,SALAKHUTDINOV R R.Reducing the dimensionality of data with neural networks [J].Science,2006,313(5786):504-507.
[26]HINTON G E,SALAKHUTDINOV R R.Reducing the dimensionality of data with neural networks[J].Science,2006,313(5786):504-507.
[27]DENG L,ABDEL-HAMID O,YU D.A deep convolutional neural network using heterogeneous pooling for trading acoustic invariance with phonetic confusion[C]∥IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP).2013:6669-6673.
[28]HINTON G E,SRIVASTAVA N,KRIZHEVSKY A,et al.Improving neural networks by preventing co-adaptating of feature detectors[J].Computer Science,2012,3(4):212-223.
[29]BA J,FREY B.Adaptive dropout for training deep neural networks[C]∥Advances in Neural Information Processing Systems(NIPS).2013:3084-3092.
[30]YANG W,JIN L,TAO D,et al.DropSample:A new training method to enhance deep convolutional neural networks for large-scale unconstrained handwritten Chinese character recognition[J].Pattern Recognition,2016,4(58):190-203.
[31]KINGMA D P,BA J.Adam:A method for stochastic opti-mization[J].arXiv:1412.6980v8,2014.
[32]PASZKE A,GROSS S,CHINTALA S,et al.PyTorch:Ten-sors and dynamic neural networks in Python with strong GPU acceleration [OL].http://pytorch.org.
[33]CHEN S J,XUE Y.Research on Real-time Dynamic Bus Scheduling Based on Unsupervised Learning [J].Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition),2019,31(2):192-199.(in Chinese)陈深进,薛洋.基于无监督学习的实时公交动态调度的研究[J].重庆邮电大学学报(自然科学版),2019,31(2):192-199.
[1] SONG Jie, LIANG Mei-yu, XUE Zhe, DU Jun-ping, KOU Fei-fei. Scientific Paper Heterogeneous Graph Node Representation Learning Method Based onUnsupervised Clustering Level [J]. Computer Science, 2022, 49(9): 64-69.
[2] ZHOU Le-yuan, ZHANG Jian-hua, YUAN Tian-tian, CHEN Sheng-yong. Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion [J]. Computer Science, 2022, 49(9): 155-161.
[3] CHEN Yong-quan, JIANG Ying. Analysis Method of APP User Behavior Based on Convolutional Neural Network [J]. Computer Science, 2022, 49(8): 78-85.
[4] ZHU Cheng-zhang, HUANG Jia-er, XIAO Ya-long, WANG Han, ZOU Bei-ji. Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism [J]. Computer Science, 2022, 49(8): 113-119.
[5] DAI Zhao-xia, LI Jin-xin, ZHANG Xiang-dong, XU Xu, MEI Lin, ZHANG Liang. Super-resolution Reconstruction of MRI Based on DNGAN [J]. Computer Science, 2022, 49(7): 113-119.
[6] LIU Yue-hong, NIU Shao-hua, SHEN Xian-hao. Virtual Reality Video Intraframe Prediction Coding Based on Convolutional Neural Network [J]. Computer Science, 2022, 49(7): 127-131.
[7] XU Ming-ke, ZHANG Fan. Head Fusion:A Method to Improve Accuracy and Robustness of Speech Emotion Recognition [J]. Computer Science, 2022, 49(7): 132-141.
[8] YANG Yue, FENG Tao, LIANG Hong, YANG Yang. Image Arbitrary Style Transfer via Criss-cross Attention [J]. Computer Science, 2022, 49(6A): 345-352.
[9] YANG Jian-nan, ZHANG Fan. Classification Method for Small Crops Combining Dual Attention Mechanisms and Hierarchical Network Structure [J]. Computer Science, 2022, 49(6A): 353-357.
[10] WU Zi-bin, YAN Qiao. Projected Gradient Descent Algorithm with Momentum [J]. Computer Science, 2022, 49(6A): 178-183.
[11] ZHANG Jia-hao, LIU Feng, QI Jia-yin. Lightweight Micro-expression Recognition Architecture Based on Bottleneck Transformer [J]. Computer Science, 2022, 49(6A): 370-377.
[12] WANG Jian-ming, CHEN Xiang-yu, YANG Zi-zhong, SHI Chen-yang, ZHANG Yu-hang, QIAN Zheng-kun. Influence of Different Data Augmentation Methods on Model Recognition Accuracy [J]. Computer Science, 2022, 49(6A): 418-423.
[13] SUN Jie-qi, LI Ya-feng, ZHANG Wen-bo, LIU Peng-hui. Dual-field Feature Fusion Deep Convolutional Neural Network Based on Discrete Wavelet Transformation [J]. Computer Science, 2022, 49(6A): 434-440.
[14] ZHAO Zheng-peng, LI Jun-gang, PU Yuan-yuan. Low-light Image Enhancement Based on Retinex Theory by Convolutional Neural Network [J]. Computer Science, 2022, 49(6): 199-209.
[15] HU Fu-yuan, WAN Xin-jun, SHEN Ming-fei, XU Jiang-lang, YAO Rui, TAO Zhong-ben. Survey Progress on Image Instance Segmentation Methods of Deep Convolutional Neural Network [J]. Computer Science, 2022, 49(5): 10-24.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.