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A Markov Chain Based Demand Prediction Model for Stations in Bike Sharing Systems

Author

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  • Yajun Zhou
  • Lilei Wang
  • Rong Zhong
  • Yulong Tan

Abstract

Accurate transfer demand prediction at bike stations is the key to develop balancing solutions to address the overutilization or underutilization problem often occurring in bike sharing system. At the same time, station transfer demand prediction is helpful to bike station layout and optimization of the number of public bikes within the station. Traditional traffic demand prediction methods, such as gravity model, cannot be easily adapted to the problem of forecasting bike station transfer demand due to the difficulty in defining impedance and distinct characteristics of bike stations (Xu et al. 2013). Therefore, this paper proposes a prediction method based on Markov chain model. The proposed model is evaluated based on field data collected from Zhongshan City bike sharing system. The daily production and attraction of stations are forecasted. The experimental results show that the model of this paper performs higher forecasting accuracy and better generalization ability.

Suggested Citation

  • Yajun Zhou & Lilei Wang & Rong Zhong & Yulong Tan, 2018. "A Markov Chain Based Demand Prediction Model for Stations in Bike Sharing Systems," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-8, January.
    • Handle: RePEc:hin:jnlmpe:8028714
    DOI: 10.1155/2018/8028714
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    Cited by:

    1. Zhou, Yu & Chen, Yang & Liu, Shenyan & Kou, Gang, 2024. "Availability simulation and transfer prediction for bike sharing systems based on discrete event simulation," Socio-Economic Planning Sciences, Elsevier, vol. 94(C).
    2. Guo, Bao & Li, Minglun & Zhou, Mengnan & Zhang, Fan & Wang, Pu, 2023. "A new anomalous travel demand prediction method combining Markov model and complex network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 619(C).
    3. Yousif Alyousifi & Kamarulzaman Ibrahim & Mahmod Othamn & Wan Zawiah Wan Zin & Nicolas Vergne & Abdullah Al-Yaari, 2022. "Bayesian Information Criterion for Fitting the Optimum Order of Markov Chain Models: Methodology and Application to Air Pollution Data," Mathematics, MDPI, vol. 10(13), pages 1-16, June.
    4. Paweł Więcek & Daniel Kubek, 2024. "The Impact Time Series Selected Characteristics on the Fuel Demand Forecasting Effectiveness Based on Autoregressive Models and Markov Chains," Energies, MDPI, vol. 17(16), pages 1-18, August.
    5. Sujae Kim & Sangho Choo & Gyeongjae Lee & Sanghun Kim, 2022. "Predicting Demand for Shared E-Scooter Using Community Structure and Deep Learning Method," Sustainability, MDPI, vol. 14(5), pages 1-15, February.

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