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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 7
2025-2031

Prediction of short-time passenger flow on multi-station urban rail based on SAE-ConvLSTM deep learning model

Li Sha1
Wang Qiuwen2
Chen Yanru1
Qin Juan1
1. College of Economics & Management, Southwest Jiaotong University, Chengdu 610031, China
2. College of Economics & Management, West Yunnan University, Lincang Yunnan 677000, China
DOI: 10.19734/j.issn.1001-3695.2021.12.0678

Abstract

In order to accurately predict the short-term passenger flow of urban rail transit for multiple stations, this paper proposed a deep learning model, SAE-ConvLSTM, combining convolutional long short-term memory(ConvLSTM) and stack autoencoder(SAE). This paper considered thirteen external factors related to passenger flow, whose features would be extracted by SAE with successive layers and thus obtain more representative features. It proposed ConvLSTM to extract spatiotemporal features of passenger flow, which was combined with the resulting external factors to predict short-term passenger flow of multiple stations simultaneously. And it developed latent action Monte Carlo tree search(LA-MCTS) to optimize the parameters of SAE. Compared with genetic algorithm(GA), particle swarm optimization(PSO), simulated annealing algorithm(SA) and tabu search(TS), LA-MCTS performed best in terms of effect and efficiency. This paper conducted extensive experiments. The results show that SAE-ConvLSTM works better than shallow machine learning model—back propagation neural network(BPNN), support vector regression mode(SVR), autoregressive integrated moving average model(ARIMA), and deep learning model—long and short time memory network(LSTM), convolutional neural network(CNN) and ConvLSTM without external features, ConvLSTM external features without SAE, CNN+LSTM and CNN+LSTM with external features, in terms of root mean square errors(RMSE), mean absolute errors(MAE) and mean absolute percentage errors(MAPE), and the goodness of fit(R2).

Foundation Support

国家重点研发计划资助项目(2018YFC0705000)
西南交通大学经济管理学院资助项目(JGSF06)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.12.0678
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 7
Section: Algorithm Research & Explore
Pages: 2025-2031
Serial Number: 1001-3695(2022)07-016-2025-07

Publish History

[2022-03-08] Accepted Paper
[2022-07-05] Printed Article

Cite This Article

李莎, 王秋雯, 陈彦如, 等. 基于SAE-ConvLSTM深度学习模型的多站城轨短时客流预测 [J]. 计算机应用研究, 2022, 39 (7): 2025-2031. (Li Sha, Wang Qiuwen, Chen Yanru, et al. Prediction of short-time passenger flow on multi-station urban rail based on SAE-ConvLSTM deep learning model [J]. Application Research of Computers, 2022, 39 (7): 2025-2031. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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