A multi-pattern deep fusion model for short-term bus passenger flow forecasting

Applied Soft Computing Journal

Volumn 58, Issue , 2017, Pages 669-680

  Bai, Yun ^a   Sun, Zhenzhong ^a   Zeng, Bo ^a   Deng, Jun ^a   Li, Chuan ^a  

^a DONGGUAN UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Affinity propagation; Deep belief network; Forecast; Multi pattern deep fusion; Short term bus passenger flow

Indexed keywords

BAYESIAN NETWORKS; BUSES; FORECASTING; INFORMATION MANAGEMENT; MEAN SQUARE ERROR;

AFFINITY PROPAGATION; DEEP BELIEF NETWORKS; DETERMINATION COEFFICIENTS; MEAN ABSOLUTE PERCENTAGE ERROR; MULTI PATTERNS; PASSENGER FLOWS; ROOT MEAN SQUARE ERRORS; TRANSPORTATION PLANNING;

TRANSPORTATION;

EID: 85019769810     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2017.05.011     Document Type: Article

References (42)

1. Wei, Y., Chen, M.C., Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks. Transp. Res. C 21 (2012), 148–162.
2. Box, G.E.P., Jenkins, G.M., Reinsel, G.C., Time Series Analysis: Forecasting and Control. 2013, Wiley, Hoboken, NJ.
3. Anvari, S., Tuna, S., Canci, M., Turky, M., Automated Box-Jenkins forecasting tool with an application for passenger demand in urban rail systems. J. Adv. Transp. 50:1 (2015), 25–49.
4. Milenković, M., Švadlenka, L., Melichar, V., Bojović, N., Avramović, Z., SARIMA modelling appoach for rail way passenger flow forecasting. Transport, 2016, 10.3846/16484142.2016.1139623.
5. Gan, M., Cheng, Y., Liu, K., Zhang, G., Seasonal and trend time series forecasting based on a quasi-linear autoregressive model. Appl. Soft Comput. 24 (2014), 13–18.
6. Hofleitner, A., Herring, R., Bayen, A., Arterial travel time forecast with streaming data: a hybrid approach of flow modeling and machine learning. Transp. Res. B 46 (2012), 1097–1122.
7. Dou, F., Xu, J., Wang, L., Jia, L., A train dispatching model based on fuzzy passenger demand forecasting during holidays. J. Ind. Eng. Manage. 6:1 (2013), 320–335.
8. Guo, J., Huang, W., Williams, B.M., Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification. Transp. Res. C 43 (2014), 50–64.
9. Jiang, X., Zhang, L., Chen, X., Short-term forecasting of high-speed rail demand: a hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in China. Transp. Res. C 44 (2014), 110–127.
10. Chen, R., Liang, C.Y., Hong, W.C., Gu, D.X., Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm. Appl. Soft Comput. 26 (2015), 435–443.
11. Sun, Y., Leng, B., Guan, W., A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system. Neurocomputing 166 (2015), 109–121.
12. Babu, C., Reddy, B., A moving-average filter based hybrid ARIMA-ANN model for forecasting time series data. Appl. Soft Comput. 23 (2014), 27–38.
13. Xie, G., Wang, S., Lai, K.K., Short-term forecasting of air passenger by using hybrid seasonal decomposition and least squares support vector regression approaches. J. Air Transp. Manage. 37 (2014), 20–26.
14. Glišović, N., Milenković, M., Bojović, N., Švadlenka, L., Avramović, Z., A hybrid model for forecasting the volume of passenger flows on Serbian railways. Oper. Res. 16:2 (2016), 271–285.
15. Lee, S., Lee, Y.I., Cho, B., Short-term travel speed prediction models in car navigation systems. J. Adv. Transp. 40:2 (2006), 123–139.
16. Tsai, T.H., Lee, C.K., Wei, C.H., Neural network based temporal feature models for short-term railway passenger demand forecasting. Expert Syst. Appl. 36:2 (2009), 3728–3736.
17. Zhang, X., Mao, B., Wang, Y., Feng, J., Li, M., Wavelet neural network-based short-term passenger flow forecasting on urban rail transit. Telkomnika Indonesian J. Electr. Eng. 11 (2013), 7379–7385.
18. Chen, Y., Yang, B., Meng, Q., Small-time scale network traffic prediction based on flexible neural tree. Appl. Soft Comput. 12:1 (2012), 274–279.
19. Bai, Y., Wang, P., Xie, J.J., Li, J., Li, C., An additive model for monthly reservoir inflow forecast. J. Hydrol. Eng., 20(7), 2015, 04014079.
20. Ma, Z.L., Xing, J.P., Mesbah, M., Ferreira, L., Predicting short-term bus passenger demand using a pattern hybrid approach. Transp. Res. C 39 (2014), 148–163.
21. Nadezda, Z., Andrejs, R., Yuri, M., Transport simulation model calibration with two-step cluster analysis procedure. Inf. Technol. Manage. Sci. 18:1 (2015), 49–56.
22. Frey, B.J., Dueck, D., Clustering by passing messages between data points. Science 305:5814 (2007), 972–976.
23. Leone, M., Weigt, S.M., Clustering by soft-constraint affinity propagation: applications to gene-expression data. Bioinformatics 23:20 (2007), 2708–2715.
24. Su, H., Sheng, Y., Du, P., Liu, K., Adaptive affinity propagation with spectral angle mapper for semi-supervised hyperspectral band selection. Appl. Opt. 51:14 (2012), 2656–2663.
25. Shang, F., Jiao, L.C., Shi, J., Wang, F., Gong, M.G., Fast affinity propagation clustering: a multilevel approach. Pattern Recognit. 45 (2012), 474–486.
26. Hinton, G.E., Osindero, S., Teh, Y.W., A fast learning algorithm for deep belief nets. Neural Comput. 18:7 (2006), 1527–1554.
27. Bengio, Y., Learning deep architectures for AI. Found. Trends Mach. Learn. 2 (2009), 1–127.
28. Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H., Greedy layer-wise training of deep networks. Adv. Neural Inf. Process. Syst. 19 (2007), 153–160.
29. Li, C., Bai, Y., Zeng, B., Deep feature learning architectures for daily reservoir inflow forecasting. Water Resour. Manage. 30:14 (2016), 5145–5161.
30. Li, C., Cabrera, D., Oliveira, J., Sanchez, R.V., Cerrada, M., Zurita, G., Extracting repetitive transients for rotating machinery diagnosis using multiscale clustered grey infogram. Mech. Syst. Sig. Process. 76–77 (2016), 157–173.
31. Rahhal, M.M.A., Bazi, Y., Alhichri, H., Alajlan, N., Melgani, F., Yager, R.R., Deep learning approach for active classification of electrocardiogram signals. Inf. Sci. 345 (2016), 340–354.
32. Li, C., Sanchez, R.V., Zurita, G., Cerrada, M., Cabrera, D., Vásquez, R., Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals. Mech. Syst. Sig. Process. 76–77 (2016), 283–293.
33. Bai, Y., Chen, Z.Q., Xie, J.J., Li, C., Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models. J. Hydrol. 532 (2016), 193–206.
34. Huang, W., Hong, H., Li, M., Hu, W., Song, G., Xie, K., Deep architecture for traffic flow prediction. Int. Conf. Adv. Data Min. Appl. Springer-Verlag, 2013, 165–176.
35. Huang, W., Song, G., Hong, H., Xie, K., Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans. Intell. Transp. Syst. 15:5 (2014), 2191–2201.
36. Refianti, R., Mutiara, A.B., Syamsudduha, A.A., Performance evaluation of affinity propagation approaches on data clustering. Int. J. Adv. Comput. Sci. Appl. 7:3 (2016), 420–429.
37. Xu, X.Y., Liu, J., Li, H.Y., Hu, J.Q., Analysis of subway station capacity with the use of queueing theory. Transp. Res. C 38 (2014), 28–43.
38. Y. Bengio, A. Courville, P. Vincent, Unsupervised feature learning and deep learning: A review and new perspectives Cornell University Library 2014 arXiv:1206.5538v3.
39. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A., Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11 (2010), 3371–3408.
40. Längkvist, M., Karlsson, L., Loutfi, A., A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognit. Lett. 42:6 (2014), 11–24.
41. Janez, D., 2006: Statistical comparisons of classifiers over multiple datasets. J. Mach. Learn. Res. 7 (2016), 1–30.
42. Diebold, F.X., Mariano, R.S., Comparing predictive accuracy. J. Bus. Econ. Stat. 13:3 (1995), 253–263.