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A dynamic line generation and vehicle scheduling method for airport bus line based on multi-source big travel data

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Abstract

Airport bus is an important public transportation mode for large international airport. To improve the bus station coverage, passenger demand compatibility and the scheduling flexibility of Beijing International Airport bus line, a dynamic line generation and vehicle scheduling method is proposed in this paper. Firstly, based on multi-source big data from the airport (including data from taxi, ride-hailing service, subway, regular bus, airport bus, etc.), we accurately extract candidate stations, which are very popular with passengers and convenient for parking and transfer, through public transportation demand level calculation, iterative clustering and POI matching. Then, the candidate stations need to be partitioned appropriately by selecting suitable features and calculating the similarity of candidate stations, so as to make the stations within each group a moderate size and have a consistent spatial orientation. Finally, a line generation and vehicle scheduling algorithm, which is compatible with multi-vehicle, high success rate of ride-sharing matching and low cost, is designed to realize accurate and rapid operation scheduling within each group according to the situation of passengers booking tickets. We have carried out experiments in Wangjing and Yayuncun, and the results show that our method can satisfy passenger demand fast and accurately.

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Acknowledgements

This work was partly supported by Science and Technology Plan Project of Beijing (No. Z171100005117001), partly by the Program of Beijing Outstanding Talent Training Foundation (No. 2017000021063G008) and partly by Grants from the National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology.

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Authors and Affiliations

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

    Haitao Yu & Weifeng Lv

  2. Beijing Transportation Information Center, Beijing, 100161, China

    Haitao Yu, Xiaoning Fu & Randong Xiao

  3. Beijing Key Laboratory for Comprehensive Traffic Operation Monitoring and Service, Beijing, 100161, China

    Haitao Yu, Xiaoning Fu & Randong Xiao

  4. College of Software Engineering, Beihang University, Beijing, 100191, China

    Hangou Liu

Authors
  1. Haitao Yu
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  2. Weifeng Lv
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  3. Hangou Liu
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  4. Xiaoning Fu
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  5. Randong Xiao
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Corresponding author

Correspondence to Randong Xiao.

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Communicated by X. Li.

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Yu, H., Lv, W., Liu, H. et al. A dynamic line generation and vehicle scheduling method for airport bus line based on multi-source big travel data. Soft Comput 24, 6329–6344 (2020). https://doi.org/10.1007/s00500-019-03987-4

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  • DOI: https://doi.org/10.1007/s00500-019-03987-4

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