Mass Rapid Transit System Passenger Traffic Forecast Using a Re-Sample Recurrent Neural Network

被引:5
作者
Hu, Rong [1 ]
Chiu, Yi-Chang [2 ]
Hsieh, Chih-Wei [2 ]
Chang, Tang-Hsien [3 ]
Xue, Xingsi [4 ]
Zou, Fumin [1 ]
Liao, Lyuchao [1 ]
机构
[1] Fujian Univ Technol, Fujian Prov Key Lab Automot Elect & Elect Drive, Fuzhou 350118, Fujian, Peoples R China
[2] Univ Arizona, Dept Civil & Architectural Engn & Mech, Tucson, AZ 85721 USA
[3] Fujian Univ Technol, Sch Transportat, Fuzhou 350108, Fujian, Peoples R China
[4] Fujian Univ Technol, Comp Sci & Technol, Fuzhou 350108, Fujian, Peoples R China
关键词
FLOW PREDICTION;
D O I
10.1155/2019/8943291
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
In this study, we developed a model re-sample Recurrent Neural Network (RRNN) to forecast passenger traffic on Mass Rapid Transit Systems (MRT). The Recurrent Neural Network was applied to build a model to perform passenger traffic prediction, where the forecast task was transformed into a classification task. However, in this process, the training dataset usually ended up being imbalanced. To address this dataset imbalance, our research proposes re-sample Recurrent Neural Network. A case study of the California Mass Rapid Transit System revealed that the model introduced in this work could timely and effectively predict passenger traffic of MRT. The measurements of passenger traffic themselves were also studied and showed that the new method provided a good understanding of the level of passenger traffic and was able to achieve prediction accuracy upwards of 90% higher than standard tests. The development of this model adds value to the methodology of traffic applications by employing these Recurrent Neural Networks.
引用
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页数:14
相关论文
共 30 条
[1]  
[Anonymous], P EUR CHIN C INT DAT
[2]   A moving-average filter based hybrid ARIMA-ANN model for forecasting time series data [J].
Babu, C. Narendra ;
Reddy, B. Eswara .
APPLIED SOFT COMPUTING, 2014, 23 :27-38
[3]   A multi-pattern deep fusion model for short-term bus passenger flow forecasting [J].
Bai, Yun ;
Sun, Zhenzhong ;
Zeng, Bo ;
Deng, Jun ;
Li, Chuan .
APPLIED SOFT COMPUTING, 2017, 58 :669-680
[4]   A NEURAL NETWORK BASED TRAFFIC-FLOW PREDICTION MODEL [J].
Cetiner, B. Gueltekin ;
Sari, Murat ;
Borat, Oguz .
MATHEMATICAL & COMPUTATIONAL APPLICATIONS, 2010, 15 (02) :269-278
[5]   Use of sequential learning for short-term traffic flow forecasting [J].
Chen, H ;
Grant-Muller, S .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2001, 9 (05) :319-336
[6]   Multi-column deep neural network for traffic sign classification [J].
Ciresan, Dan ;
Meier, Ueli ;
Masci, Jonathan ;
Schmidhuber, Juergen .
NEURAL NETWORKS, 2012, 32 :333-338
[7]  
Collobert R., 2008, P 25 INT C MACHINE L, P160, DOI [10.1145/1390156.1390177, DOI 10.1145/1390156.1390177]
[8]   An adaptive information fusion model to predict the short-term link travel time distribution in dynamic traffic networks [J].
Du, Lili ;
Peeta, Srinivas ;
Kim, Yong Hoon .
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL, 2012, 46 (01) :235-252
[9]  
Gliovi N., 2016, OPERATIONAL RES, V16, P271
[10]  
Graves A, 2013, INT CONF ACOUST SPEE, P6645, DOI 10.1109/ICASSP.2013.6638947