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Enhancing Sustainable Transportation: AI-Driven Bike Demand Forecasting in Smart Cities

Author

Listed:
  • Malliga Subramanian

    (Department of Computer Science and Engineering, Kongu Engineering College, Erode 638060, India)

  • Jaehyuk Cho

    (Department of Software Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea)

  • Sathishkumar Veerappampalayam Easwaramoorthy

    (Department of Software Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea)

  • Akash Murugesan

    (Department of Computer Science and Engineering, Kongu Engineering College, Erode 638060, India)

  • Ramya Chinnasamy

    (Department of Computer Science and Engineering, Kongu Engineering College, Erode 638060, India)

Abstract

Due to global ecological restrictions, cities, particularly urban transportation, must choose ecological solutions. Sustainable bike-sharing systems (BSS) have become an important element in the worldwide transportation infrastructure as an alternative to fossil-fuel-powered cars in metropolitan areas. Nevertheless, the placement of docks, which are the parking areas for bikes, depends on accessibility to bike paths, population density, difficulty in bike mobility, commuting cost, the spread of docks, and route imbalance. The purpose of this study is to compare the performance of various time series and machine learning algorithms for predicting bike demand using a two-year historical log from the Capital Bikeshare system in Washington, DC, USA. Specifically, the algorithms tested are LSTM, GRU, RF, ARIMA, and SARIMA, and their performance is then measured using the MSE, MAE, and RMSE metrics. The study found GRU performed the best, with RF also producing reasonably accurate predictions. ARIMA and SARIMA models produced less accurate predictions, likely due to their assumptions of linearity and stationarity in the data. In summary, this research offers significant insights into the efficacy of diverse algorithms in forecasting bike demand, thereby contributing to future research in the field.

Suggested Citation

  • Malliga Subramanian & Jaehyuk Cho & Sathishkumar Veerappampalayam Easwaramoorthy & Akash Murugesan & Ramya Chinnasamy, 2023. "Enhancing Sustainable Transportation: AI-Driven Bike Demand Forecasting in Smart Cities," Sustainability, MDPI, vol. 15(18), pages 1-19, September.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13840-:d:1241918
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    References listed on IDEAS

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    1. Schuijbroek, J. & Hampshire, R.C. & van Hoeve, W.-J., 2017. "Inventory rebalancing and vehicle routing in bike sharing systems," European Journal of Operational Research, Elsevier, vol. 257(3), pages 992-1004.
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    3. Todd Litman & David Burwell, 2006. "Issues in sustainable transportation," International Journal of Global Environmental Issues, Inderscience Enterprises Ltd, vol. 6(4), pages 331-347.
    4. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
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