Abstract
The accurate passenger flow prediction is the base of bus scheduling and bus dispatching. Many factors, including internal factors and external factors, have great impact on the fluctuation of passenger flow. In the modern informationized bus system, many influencing factors became available by multi-source data. Current passenger flow prediction methods are mainly based on statistical predicting methods and machine learning methods. The implication of interpolating prediction method on passenger flow prediction is preliminary. Interpolating prediction method makes use of historical data; the prediction result is generally accurate, and the method is robust. Interpolating prediction method shows good performance and has mature application in other research areas. This paper makes use of historical passenger data and multi-source data; apply Shepard model to predict public transportation passenger flow. The result shows that Shepard prediction model has better performance than that of neural network (NN) model and support vector machine (SVM) model. The mean absolute percentage error (MAPE) has increased 7.5 and 3.43%; the MSP has increased 16 and 10.51% compared with NN and SVM and has lower dependency of parameters.
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References
Wu W (2015) The robustness and control strategies of bus network schedule coordination with uncertainty. South China University of Technology
Liu C, Zhang YQ, Chen HR (2008) Transit stations temporal getting on/off flow forecasting model based on BP neural network. Transp Res 5:186–189
Jiang P, Shi Q, Chen WW (2009) Forecast of passenger volume based on neutral network. J Wuhan Univ Technol (Transp Sci Eng) 33(3):414–417
Deng H, Zhu X, Zhang Q (2012) Prediction of short-term pubic transportation flow based on multiple-kernel least square support vector machine. J Transp Eng Inf 10(2):84–88
Liu K, Li W, Zhao J (2010) Study on wavelet forecast method for short-term passenger flow. J Transp Eng Inf 8(2):111–117
Zhao S, Ni T, Wang Y (2011) A new approach to the prediction of passenger flow in a transit system. Comput Math Appl 61(8):1968–1974
Ren CL, Cao CX, Li J (2011) Research for short-term passenger flow forecasting based on wavelet neural network. Sci Technol Eng 11(21):5099–5103
Zhang C, Song R, Sun Y (2011) Kalman filter-based short-term passenger flow forecasting on bus stop. J Transp Syst Eng Inf Technol 11(4):154–159
Gu Y, Han Y, Fang XL (2011) Method of hub station passenger flow forecasting based on ARMA model. J Transp Inf Saf 29(2):5–9
Wei Y, Chen MC (2012) Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks. Transp Res Part C Emerg Technol 21(1)
Liang XL (2012) Prediction method research of dynamic mass transit passenger flow. Urban Public Transp 4:33–34
Wang QR, Zhang QY (2012) Forecasting of short-term urban public transit volume based on random gray ant colony neural network. Appl Res Comput 29(6):2078–2080
Wu WT, Jin WZ, Lin PQ (2011) The method of traffic state identification based on BP neural network. J Transp Inf Saf 29(4):71–74
Castro-Neto M, Jeong YS, Jeong MK (2009) Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Syst Appl 36(3):6164–6173
Tan HC, Feng GD, Feng JS (2013) A tensor-based method for missing traffic data completion. Transp Res Part C Emerg Technol 28(3):15–27
Tan HC, Wu YK, Shen B (2015) Short-term traffic prediction based on dynamic tensor completion. Beijing Institute of Technology
Zhong EJ, Huang TZ (2004) Numerical analysis
Jin JL, Wei YM, Ding J (2002) Shepard interpolation model for predicting annual runoff. J Yangtze River Sci Res Inst 19(1):52–55
Zhang F, Lv ZY, Zhao XP (2010) Novel method based on sequence Shepard interpolation for structual reliability analysis. J Mech Eng 46(10):176–181
Murphy KP (2012) Machine learning: a probabilistic perspective. MIT Press
Hu D, Cai Y, Xing Y (2008) On sensitivity analysis. J Beijing Norm Univ (Nat Sci) (01):9–16
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Jin, W., Li, P., Wu, W., Wei, L. (2019). Short-Term Public Transportation Passenger Flow Forecasting Method Based on Multi-source Data and Shepard Interpolating Prediction Method. In: Long, S., Dhillon, B. (eds) Man-Machine-Environment System Engineering . MMESE 2018. Lecture Notes in Electrical Engineering, vol 527. Springer, Singapore. https://doi.org/10.1007/978-981-13-2481-9_33
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DOI: https://doi.org/10.1007/978-981-13-2481-9_33
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