Short-term prediction of passenger volume for urban rail systems: A deep learning approach based on smart-card data

Abstract Short-term prediction of passenger volume is a complex but critical task to urban rail companies, which desire prediction methods with high accuracy, time efficiency and good practicality. Good prediction results of the outbound passenger volume at urban rail stations are important to the organization of passenger flow, and helpful to the arrangement of shuttles, especially in large transit junctions. The application of automatic fare collection (AFC) devices in urban rail transit systems helps to collect large amounts of historical data of completed journeys, which can be used by metro operators to construct a database of the urban rail passenger volume. Based on deep learning techniques and big data, this paper develops an improved spatiotemporal long short-term memory model (Sp-LSTM) to forecast short-term outbound passenger volume at urban rail stations. The proposed model predicts the outbound passenger volume on the basis of the historical data of the spatial-temporal passenger volume, station origin–destination (OD) matrix and the operation data of the rail transit network. Finally, based on actual data of the Beijing Metro Airport Line, a case study is carried out to compare the proposed Sp-LSTM with other prediction methods, i.e., the general long short-term memory model (LSTM), the autoregressive integrated moving average model (ARIMA), and the non-linear autoregressive neural network model (NAR), and the results show that the proposed method outperforms the others.

[1]  Mu-Chen Chen,et al.  Exploring time variants for short-term passenger flow , 2011 .

[2]  Billy M. Williams,et al.  Urban Freeway Traffic Flow Prediction: Application of Seasonal Autoregressive Integrated Moving Average and Exponential Smoothing Models , 1998 .

[3]  Dmitry Pavlyuk,et al.  Short-term Traffic Forecasting Using Multivariate Autoregressive Models , 2017 .

[4]  Der-Horng Lee,et al.  Sequence to sequence learning with attention mechanism for short-term passenger flow prediction in large-scale metro system , 2019, Transportation Research Part C: Emerging Technologies.

[5]  Taghi M. Khoshgoftaar,et al.  Deep learning applications and challenges in big data analytics , 2015, Journal of Big Data.

[6]  Mu-Chen Chen,et al.  Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks , 2012 .

[7]  Li Li,et al.  Using LSTM and GRU neural network methods for traffic flow prediction , 2016, 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC).

[8]  Wei Deng,et al.  New Bayesian combination method for short-term traffic flow forecasting , 2014 .

[9]  Ming-wei Hu.  A Survey and Simulation of Passenger Flow Organization of the Shenzhen Urban Rail Transit Station , 2011 .

[10]  B. Han,et al.  Urban Rail Transit in China: Progress Report and Analysis (2008–2015) , 2016 .

[11]  Jianjun Wu,et al.  Urban Rail Timetable Optimization to Improve Operational Efficiency with Flexible Routing Plans: A Nonlinear Integer Programming Model , 2019, Sustainability.

[12]  Rung-Ching Chen,et al.  A novel passenger flow prediction model using deep learning methods , 2017 .

[13]  Yang Liu,et al.  DeepPF: A deep learning based architecture for metro passenger flow prediction , 2019, Transportation Research Part C: Emerging Technologies.

[14]  Yang Zhao,et al.  Forecasting Short-Term Passenger Flow: An Empirical Study on Shenzhen Metro , 2019, IEEE Transactions on Intelligent Transportation Systems.

[15]  Alex Lu,et al.  Entry-Only Automated Fare-Collection System Data Used to Infer Ridership, Rider Destinations, Unlinked Trips, and Passenger Miles , 2009 .

[16]  Huachun Tan,et al.  Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework , 2016, ArXiv.

[17]  Nicholas G. Polson,et al.  Deep Learning Predictors for Traffic Flows , 2016 .

[18]  Xin Yang,et al.  Optimizing the release of passenger flow guidance information in urban rail transit network via agent-based simulation , 2019, Applied Mathematical Modelling.

[19]  Chi-Kang Lee,et al.  Neural network based temporal feature models for short-term railway passenger demand forecasting , 2009, Expert Syst. Appl..

[20]  Zejian Deng,et al.  Trajectory Planning and Tracking for Autonomous Vehicle Based on State Lattice and Model Predictive Control , 2019, IEEE Intelligent Transportation Systems Magazine.

[21]  Ziyou Gao,et al.  Recognizing the Critical Stations in Urban Rail Networks: An Analysis Method Based on the Smart-Card Data , 2019, IEEE Intelligent Transportation Systems Magazine.

[22]  Hong Kam Lo,et al.  Energy minimization in dynamic train scheduling and control for metro rail operations , 2014 .

[23]  Byoung-Jo Yoon,et al.  Dynamic near-term traffic flow prediction: system- oriented approach based on past experiences , 2012 .

[24]  陈 彦莉.  Prediction of Shanghai Metro Line 16 Passenger Flow Based on Time Series Analysis—with Lingang Avenue Station as a Study Case , 2016 .

[25]  Qihui Wu,et al.  A survey of machine learning for big data processing , 2016, EURASIP Journal on Advances in Signal Processing.

[26]  Fotios Petropoulos,et al.  Forecasting with multivariate temporal aggregation: the case of promotional modelling , 2016 .

[27]  A. R. Cook,et al.  ANALYSIS OF FREEWAY TRAFFIC TIME-SERIES DATA BY USING BOX-JENKINS TECHNIQUES , 1979 .

[28]  Gabriel Trierweiler Ribeiro,et al.  Agribusiness time series forecasting using Wavelet neural networks and metaheuristic optimization: An analysis of the soybean sack price and perishable products demand , 2018, International Journal of Production Economics.

[29]  Zhonghui Chen,et al.  Short-term traffic flow prediction with Conv-LSTM , 2017, 2017 9th International Conference on Wireless Communications and Signal Processing (WCSP).

[30]  Pengpeng Jiao,et al.  Three Revised Kalman Filtering Models for Short-Term Rail Transit Passenger Flow Prediction , 2016 .

[31]  Haiying Li,et al.  Short-term passenger flow prediction under passenger flow control using a dynamic radial basis function network , 2019, Appl. Soft Comput..

[32]  Bin Ran,et al.  Short-to-medium Term Passenger Flow Forecasting for Metro Stations using a Hybrid Model , 2017, KSCE Journal of Civil Engineering.

[33]  Biao Leng,et al.  A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system , 2015, Neurocomputing.

[34]  Hong Kam Lo,et al.  An energy-efficient scheduling and speed control approach for metro rail operations , 2014 .

[35]  Yang Wang,et al.  A new approach to the prediction of passenger flow in a transit system , 2011, Comput. Math. Appl..

[36]  Enjian Yao,et al.  Holiday Passenger Flow Forecasting Based on the Modified Least-Square Support Vector Machine for the Metro System , 2017 .

[37]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[38]  Der-Horng Lee,et al.  Short-term freeway traffic flow prediction : Bayesian combined neural network approach , 2006 .

[39]  William H. K. Lam,et al.  A study of crowding effects at the Hong Kong light rail transit stations , 1999 .

[40]  Fei-Yue Wang,et al.  Traffic Flow Prediction With Big Data: A Deep Learning Approach , 2015, IEEE Transactions on Intelligent Transportation Systems.

[41]  Wenhao Huang,et al.  Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning , 2014, IEEE Transactions on Intelligent Transportation Systems.

[42]  Jürgen Schmidhuber,et al.  Learning to Forget: Continual Prediction with LSTM , 2000, Neural Computation.