Empirical mode decomposition based long short-term memory neural network forecasting model for the short-term metro passenger flow


Autoři: Quanchao Chen aff001;  Di Wen aff001;  Xuqiang Li aff001;  Dingjun Chen aff001;  Hongxia Lv aff001;  Jie Zhang aff001;  Peng Gao aff001
Působiště autorů: School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China aff001;  National Railway Train Diagram Research and Training Center, Southwest Jiaotong University, Chengdu, China aff002;  National and Local Joint Engineering Laboratory of Comprehensive Intelligent Transportation, Southwest Jiaotong University, Chengdu, China aff003
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: 10.1371/journal.pone.0222365

Souhrn

Short-term metro passenger flow forecasting is an essential component of intelligent transportation systems (ITS) and can be applied to optimize the passenger flow organization of a station and offer data support for metro passenger flow early warning and system management. LSTM neural networks have recently achieved remarkable recent in the field of natural language processing (NLP) because they are well suited for learning from experience to predict time series. For this purpose, we propose an empirical mode decomposition (EMD)-based long short-term memory (LSTM) neural network model for predicting short-term metro inbound passenger flow. The EMD algorithm decomposes the original sequential passenger flow into several intrinsic mode functions (IMFs) and a residual. Selected IMFs that are strongly correlated with the original data can be obtained via feature selection. The selected IMFs and the original data are integrated into inputs for LSTM neural networks, and a single LSTM prediction model and an EMD-LSTM hybrid forecasting model are developed. Finally, historical real automatic fare collection (AFC) data from metro passengers are collected from Chengdu Metro to verify the validity of the proposed EMD-LSTM prediction model. The results indicate that the proposed EMD-LSTM hybrid forecasting model outperforms the LSTM, ARIMA and BPN models.

Klíčová slova:

Research and analysis methods – Mathematical and statistical techniques – Statistical methods – Physical sciences – Mathematics – Statistics – Computer and information sciences – Recurrent neural networks – Artificial intelligence – Machine learning – Support vector machines – Information technology – Natural language processing – Biology and life sciences – Neuroscience – Neural networks – Cognitive science – Cognition – Memory – Short term memory – Learning and memory – Engineering and technology – Transportation


Zdroje

1. Yao E, Zhou W, Zhang Y. Real-time forecast of entrance and exit passenger flow for newly opened station of urban rail transit at initial stage. China Railway Science. 2018; 39 (119–27).

2. Zhu Z, Weng Z. Railway passenger and freight volume forecasting based on chaos theory. Journal of The China Railway Society. 2011; 33 (1–7).

3. Zhang Y, Guo Y, Wei Y, Cheng S, Xing Z, editors. Chaotic Characteristics Identification on Terminal Departing Passenger Traffic Time Series. Applied Mechanics and Materials; 2013: Trans Tech Publ.

4. Huang Z, Feng S. Grey forecasting model in application of railway passenger flow prediction research. Technology and Economy in Areas of Communications. 2014; 16 (57–60).

5. Yang H, Liu J, Zheng B. Improvement and application of gray prediction GM (1,1) model. Mathematics in Practice and Theory. 2011; 23 (39–46).

6. Zhang J. Improved gray prediction model and its application. Xi’an University of Technology, Xi’an, China. 2008.

7. Zhang G, Patuwo B, Hu M. Forecasting with artificial neural networks: The state of the art. International journal of forecasting. 1998; 14 (35–62).

8. Ishak S, Kotha P, Alecsandru C. Optimization of dynamic neural network performance for short-term traffic prediction. In Proceedings of the 82nd Annual Meeting of the Transportation-Research-Board, Washington, DC, USA. 2003; 12–16.

9. Wu H, Zhen J, Wang Y, Wang F. Railway passenger and freight prediction based on RBF neural network theory. Journal of Railway Science and Engineering. 2014; 11 (109–114).

10. Jiang X, Adeli H. Dynamic wavelet neural network model for traffic flow forecasting. Journal of Transportation Engineering. 2005; 131 (771–779).

11. Zhang W, Shi Z, Liu Q. Research on the prediction of urban passenger transport based on support vector machine. In Proceedings of the IEE International Conference on Automation and Logistics, Jinan, China. 2007; 18–21.

12. He W, Wang Z, Jiang H. Model optimizing and feature selecting for support vector regression in time series forecasting. Neurocomputing. 2008; 72 (600–611).

13. Ghosh B, Basu B, O‘Mahony M. Multivariate short-term traffic flow forecasting using time-series analysis. IEEE Transactions on Intelligent Transportation Systems. 2009; 10 (246–254).

14. Ding A, Zhao X, Jiao L. Traffic flow time series prediction based on statistics learning theory. In Proceedings of the IEEE 5th International Conference on Intelligent Transportation Systems, Singapore, 2002; 03–06.

15. Yao X, Zhao P, Yu D. Real-time origin-destination matrices estimation for urban rail transit network based on structural state-space model. Journal of Central South University. 2015; 22 (4498–4506).

16. Pekel E, Kara S. Passenger flow prediction based on newly adopted algorithms. Applied Artificial Intelligence. 2017; 31 (64–79).

17. Li D. Passenger capacity prediction based on least squares support vector regression with continuous ant colony optimization algorithm. In Proceedings of the International Conference on Information System and Artificial Intelligence (ISAI), Hong Kong, China. 2017; 24–26.

18. Xiong J, Guan W, Sun Y. Metro transfer passenger forecasting based on Kalman filter. Journal of Beijing Jiaotong University. 2013; 37 (112–116).

19. Zhang C, Song R, Sun Y. Kalman filter-based short-term passenger flow forecasting on bus stop. Journal of Transportation Systems Engineering & Information Technology. 2011; 11 (154–159).

20. Bai Y, Sun Z, Zeng B, Deng J, Li C. A multi-pattern deep fusion model for short-term bus passenger flow forecasting. Applied Soft Computing. 2017; 58 (669–680).

21. Sun Y, Leng B, Guan W. A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system. Neurocomputing. 2015; 166 (109–121).

22. Chen Q, Li W, Zhao J. The use of LS-SVM for short-term passenger flow prediction. Transport. 2011; 26 (5–10).

23. Zhang N, Zhang Y, Wang X. Forecasting of short-term urban rail transit passenger flow with support vector machine hybrid online model. In Transportation Research Board 92nd Annual Meeting, Washington DC, United States. 2013; 13–17.

24. Gao P, Xu R. Event-driven simulation model for passenger flow in urban mass transit station. Systems Engineering-Theory & Practice. 2010; 30 (2121–2128).

25. Chen F, Chen S, Ma X. Analysis of hourly crash likelihood using unbalanced panel data mixed logit model and real-time driving environmental big data. Journal of Safety Research. 2018; 65 (153–159).

26. Ma X, Chen S, Chen F. Multivariate space-time modeling of crash frequencies by injury severity levels. Analytic Methods in Accident Research. 2017; 15 (29–40).

27. Chen F, Chen S. Injury severities of truck drivers in single- and multi-vehicle accidents on rural highway. Accident Analysis and Prevention. 2011; 43 (1677–1688).

28. Luo C, Huang C, Cao J, et al. Short-term traffic flow prediction based on least square support vector machine with hybrid optimization algorithm. Neural processing letters. 2019.

29. Li J. Study on metro short-term passenger flow forecasting based on deep learning. Southwest Jiaotong University. 2018.

30. Liu L, Chen R. A novel passenger flow prediction model using deep learning methods. Transportation Research Part C: Emerging Technologies. 2017; 84 (74–91).

31. Liu G, Yin Z, Jia Y, Xie Y. Passenger flow estimation based on convolutional neural network in public transportation system. Knowledge-Based Systems. 2017; 123 (102–115).

32. Li J, Peng H, Liu L, et al. Graph CNNs for urban traffic passenger flows prediction. In Proceedings of the 2018 IEEE SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI, Guangzhou, China. 2018; 07–11.

33. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation. 1997; 9 (1735–1780).

34. Liu Y, Qin Y, Guo J, Cai C, Wang Y, Jia L. Short-term forecasting of rail transit passenger flow based on long short-term memory neural network. In Proceedings of the International Conference on Intelligent Rail Transportation (ICIRT), Singapore. 2018; 12–17.

35. Hu Z, Zuo Y, Xue Z, Ma W, Zhang G. Predicting the metro passengers flow by long-short term memory. In Proceedings of the 12th KIPS International Conference on Ubiquitous Information Technologies and Applications (CUTE) / 9th International Conference on Computer Science and its Applications (CSA), Taichung, Taiwan. 2017; 18–20.

36. Huang N, Shen Z, Long S, Wu M, Shih H, Zheng Q, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A-Mathematical Physical and Engineering Sciences. 1998; 454 (903–995).

37. Cheng J, Yu D, Yang Y. Research on the intrinsic mode function (IMF) criterion in EMD method. Mechanical Systems and Signal Processing. 2006; 20 (817–824).

38. Wei Y, Chen M. Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks. Transportation Research Part C: Emerging Technologies. 2012; 21 (148–162).

39. Chen S, Chou W. Short-term traffic flow prediction using EMD-based recurrent Hermite neural network approach. In Proceedings of the International IEEE Conference on Intelligent Transportation Systems (ITSC), Anchorage, AK. 2012; 16–19.

40. Gers F, Schmidhuber J, Cummins F. Learning to forget: continual prediction with LSTM. In Proceedings of the Ninth International Conference on Artificial Neural Networks (ICANN), Scotland. 1999; 07–10.

41. Mandic D, Chambers J. Recurrent neural networks for prediction: learning algorithms, architectures and stability. Hoboken, NJ, USA: Wiley. 2001.

42. Liu Y, Liu Z, Jia R. Deep PF: A deep learning based architecture for metro passenger flow prediction. Transportation Research Part C: Emerging Technologies. 2019; 101 (18–34).


Článek vyšel v časopise

PLOS One


2019 Číslo 9

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