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Probability tree based passenger flow prediction and its application to the Beijing subway system

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Abstract

In order to provide citizens with safe, convenient and comfortable services and infrastructure in a metropolis, the prediction of passenger flows in the metro-net of subway system has become more important than ever before. Although a great number of prediction methods have been presented in the field of transportation, all of them belong to the station oriented approach, which is not well suited to the Beijing subway system. This paper proposes a novel metro-net oriented method, called the probability tree based passenger flow model, which is also based on historic origin-destination (OD) information. First it learns and obtains the appearance probabilities for each kind of OD pair. For the real-time origin datum, the destination datum is calculated, and then several kinds of passenger flow in the metro-net can be predicted by gathering all the contributions. The results of experiments, using the historical data of Beijing subway, show that although the proposed method has lower performance than existing prediction approaches for forecasting exit passenger flows, it is able to predict several additional kinds of passenger flow in stations and throughout the subway system; and it is a more feasible, suitable, and advanced passenger flow prediction model for Beijing subway system.

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Authors and Affiliations

  1. State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China

    Biao Leng, Zhang Xiong & Weifeng Lv

  2. School of Computer Science and Engineering, Beihang University, Beijing, 100191, China

    Biao Leng, Jiabei Zeng, Zhang Xiong & Weifeng Lv

  3. Shenzhen Key Laboratory of Data Vitalization (Smart City), Research Institute of Beihang University in Shenzhen, Shenzhen, 518057, China

    Biao Leng, Zhang Xiong & Weifeng Lv

  4. The Third Research Institute of Ministry of Public Security Run Technologies Co. Ltd., Beijing, 100044, China

    Yueliang Wan

Authors
  1. Biao Leng
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  2. Jiabei Zeng
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  3. Zhang Xiong
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  4. Weifeng Lv
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  5. Yueliang Wan
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Corresponding author

Correspondence to Weifeng Lv.

Additional information

Biao Leng received his BSc from the School of Computer Science and Technology, National University of Defense Technology, China, in 2004, and his MSc and PhD from the Department of Computer Science and Technology, Tsinghua University, Beijing, China, in 2009. He is an associate professor at the School of Computer Science and Engineering, Beihang University, China. His current research interests include intelligent transportation systems, pedestrian evacuation, 3D model retrieval, machine learning, and data mining.

Jiabei Zeng received her BE from the School of Advanced Engineering, Beihang University. Now, she is a PhD candidate in the School of Computer Science and Engineering in Beihang University. Her research areas are intelligent transportation systems and 3D model retrieval.

Zhang Xiong, professor, doctoral supervisor, dean of Sino-French Engineering School, the director of the Advanced Computer Application Research Engineering Center of National Educational Ministry, member of the National Computer Science and Technology Teaching Steering Committee of the Ministry of Education, expert of the State Leading Group Office of Golden Card Project, member of the National GFMIS Expert Consultative Committee. Xiong has made significant contributions to large-scale computer application engineerizing, and has won a first-class award in the National Science and Technology Progress Awards. His research areas are intelligent transportation systems and multimedia processing.

Weifeng Lv is a professor, the dean of School of Computer Science and Engineering, and vice director of the national laboratory of software development environment. His main research direction is large-scale software development methods and field service oriented software support technology. Lv is a member of the National Science and Technology Platform Standardization Technical Committee, the National Intelligent Transportation System Standardization Technical Committee, and the Second Beijing Information Expert Advisory Committee, and the Chinese Institute of Electronics Council.

Yueliang Wan received his BS from North China Institute of Technology, in 1995, and received MS from Nanchang University in 1998. He received his PhD from Beijing Institute of Technology in 2007. He is a principal researcher in the Third Research Institute of the Ministry of Public Security Run Technologies Co. Ltd., Beijing. He is a member of the CCF and a member of the CCF TCCS. His research interests include Web cross-media analysis and mining, massive data analysis, and data mining.

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Leng, B., Zeng, J., Xiong, Z. et al. Probability tree based passenger flow prediction and its application to the Beijing subway system. Front. Comput. Sci. 7, 195–203 (2013). https://doi.org/10.1007/s11704-013-2057-y

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  • DOI: https://doi.org/10.1007/s11704-013-2057-y

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