Accurate metro ridership prediction can guide passengers in efficiently selecting their departure time and simultaneously help traffic operators develop a passenger organization strategy. However, short-term passenger flow prediction needs to consider many factors, and the results of the existing models for short-term subway passenger flow forecasting are often unsatisfactory. Along this line, we propose a parallel architecture, called the seasonal and nonlinear least squares support vector machine (SN-LSSVM), to extract the periodicity and nonlinearity characteristics of passenger flow. Various forecasting models, including auto-regressive integrated moving average, long short-term memory network, and support vector machine, are employed for evaluating the performance of the proposed architecture. Moreover, we first applied the method to the Tiyu Xilu station which is the most crowded station in the Guangzhou metro. The results indicate that the proposed model can effectively make all-weather and year-round passenger flow predictions, thus contributing to the management of the station.
Urban Rail Transit Technical Working Committee of China Civil Engineering Society. Proposal for Urban Rail Transit Technology Development Outline (2010-2015). Urban Rapid Transit Traffic. 2010;23(06): 3-8. DOI: 10.3969/j.issn.1672-6073.2010.06.002
He Y, Sheng WW, Luo AH. Track extension, you and I travel more smoothly. People's Daily. February 3, 2019. Available from: http://paper.people.com.cn/rmrb/html/2019-02/13/nw.D110000renmrb_20190213_2-07.htm [Accessed 23rd March 2020].
Yang J. Research on Metro Passenger Flow Short-time Prediction and Evacuation Simulation. PhD thesis. Beijing Jiaotong University; 2013.
Smith BL, Demetsky MJ. Traffic flow forecasting: Comparison of modeling approaches. Journal of Transportation Engineering. 1997;123(4): 261. DOI: 10.1061/(ASCE)0733-947X(1997)123:4(261)
Yang ZS, Wang Y, Guan Q. Short-time traffic flow prediction method based on support vector machine method. Journal of Jilin University (Engineering and Technology Edition). 2006;26(6): 881-884. DOI: 10.13229/j.cnki.jdxbgxb2006.06.010
Vanajakshi L, Rilett LR. Support Vector Machine Technique for the Short Term Prediction of Travel Time. In: IEEE Intelligent Vehicles Symposium, 13-15 June 2007, Istanbul. IEEE; 2007. p. 600-605. DOI: 10.1109/IVS.2007.4290181
Wang Y, Zheng D, Luo S, Zhan DM. The research of railway passenger flow prediction model based on BP neural network. Advanced Materials Research. 2013; 605–607: 2366-2369. DOI: 10.4028/www.scientific.net/AMR.605-607.2366
Guo SY, Li WQ, Bai W, Zhang D. Prediction of Short-term Passenger Flow on a Bus Stop Based on LSSVM. Journal of Wuhan University of Technology (Transportation Science & Engineering). 2013;37(03): 603-607. DOI: 10.3963/j.issn.2095-3844.2013.03.037
Guo JY, Wang ZJ, Chen HW. On-line multi-step prediction of short term traffic flow based on GRU neural network. Proceedings of the 2nd International Conference on Intelligent Information Processing; 2017. p. 7-12. DOI: 10.1145/3144789.3144804
Wang XM, Zhang N, Zhang YL, Shi ZB. Forecasting of Short-Term Metro Ridership with Support Vector Machine Online Model. Journal of Advanced Transportation. 2018. DOI: 10.1155/2018/3189238
Kong WC, Dong ZY, Jia YW, Hill DJ. Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network. IEEE Transactions on Smart Grid. 2019;10(1): 841-851. DOI: 10.1109/TSG.2017.2753802
Lu WX, Li C. Forecasting of Short-Time Tourist Flow Based on Improved PSO Algorithm Optimized LSSVM Model. Computer Engineering and Applications. 2019;55(18): 247-255. DOI: 10.3778/j.issn.1002-8331.1807-0063
Boto GD, Díaz PFJ, González OD, et al. Wavelet-based denoising for traffic volume time series forecasting with self-organizing neural networks. Computer-Aided Civil and Infrastructure Engineering. 2010;25(7): 530-545. DOI: 10.1111/j.1467-8667.2010.00668.x
Pan L. Short-time Forecasting of High-speed Railway Passenger Flow Based on Ensemble Empirical Mode Decomposition-Gray Support vector Machine. PhD thesis. Beijing Jiaotong University; 2012.
Wei Y, Chen MC. Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks. Transportation Research Part C: Emerging Technologies. 2012;21(1): 148-162. DOI: 10.1016/j.trc.2011.06.009
Bouzerdoum M, Mellit A, Massi PA. A hybrid model (SARIMA-SVM) for short-term power forecasting of a small-scale grid-connected photovoltaic plant. Solar Energy. 2013;98: 226-235. DOI: 10.1016/j.solener.2013.10.002
Sun YX, Leng B, Guan W. A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system. Neurocomputing. 2015;166: 109-121. DOI: 10.1016/j.neucom.2015.03.085
Tang LY, Zhao Y, Cabrera J, Ma J. Forecasting Short-Term Passenger Flow: An Empirical Study on Shenzhen Metro. IEEE Transactions on Intelligent Transportation Systems. 2019;20(10): 3613-3622. DOI: 10.1109/TITS.2018.2879497
Ma XL, Zhang JY, Du BW, Ding C. Parallel Architecture of Convolutional Bi-Directional LSTM Neural Networks for Network-Wide Metro Ridership Prediction. IEEE Transactions on Intelligent Transportation Systems. 2019;20(6): 2278-2288. DOI: 10.1109/TITS.2018.2867042
Bates JM, Granger CWJ. The Combination of Forecasts. Journal of the Operational Research Society. 1969:20(4): 451-468. DOI: 10.1057/jors.1969.103
People's Network. 360 big data release Spring Festival ‘empty city index’ Beijing and Shanghai have not entered the top five. Available from: http://www.sohu.com/a/125388616_114731 [Accessed 23rd March 2020].
Sain SR, Vapnik VN. The Nature of Statistical Learning Theory. Technometrics. 1996;38(4): 409. DOI: 10.1080/00401706.1996.10484565
Vapnik VN. An overview of statistical learning theory. IEEE Transactions on Neural Networks.1999;10(5): 988-999. DOI: 10.1109/72.788640
Cherkassky V, Ma YQ. Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks. 2004;17(1): 113-126. DOI: 10.1016/S0893-6080(03)00169-2
Eberhart R, Kennedy J. A New optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 4-6 October 1995, Nagoya, Japan. IEEE; 1995. p. 39-43. DOI: 10.1109 / MHS.1995.494215
Zhu W, Wang W, Huang ZD. Estimating train choices of rail transit passengers with real timetable and automatic fare collection data. Journal of Advanced Transportation. 2017. DOI: 10.1155/2017/5824051
Zhang GP. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing. 2003;50: 159-175. DOI: 10.1016/S0925-2312(01)00702-0
Chapelle O. Training a support vector machine in the primal. Neural Computation. 2007;19(5): 1155-1178. DOI: 10.1162/neco.2007.19.5.1155
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