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Principal Component Random Forest for Passenger Demand Forecasting in Cooperative, Connected, and Automated Mobility

Author

Listed:
  • Georgios Spanos

    (Information Technologies Institute, Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece)

  • Antonios Lalas

    (Information Technologies Institute, Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece)

  • Konstantinos Votis

    (Information Technologies Institute, Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece)

  • Dimitrios Tzovaras

    (Information Technologies Institute, Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece)

Abstract

Cooperative, Connected, and Automated Mobility (CCAM) is set to play a key role in the future of transportation, contributing to the achievement of sustainable development goals. Moreover, Artificial Intelligence (AI), a transformative technology with applications across various industries, can significantly enhance CCAM operations. Additionally, passenger demand forecasting, a critical aspect of mobility research, will become even more essential as CCAM adoption continues to grow in the next years. Therefore, the present research study, in order to deal with the issue of passenger demand forecasting in CCAM, proposes the Principal Component Random Forest (PCRF) methodology, which is based on AI, as it leverages a well-established statistical methodology such as the Principal Components Analysis with a flagship traditional machine learning technique, which is Random Forest. The application of PCRF in four European pilot sites within the European Union-funded SHOW project demonstrated its high accuracy and effectiveness as reflected by the average normalized error of approximately 15%.

Suggested Citation

  • Georgios Spanos & Antonios Lalas & Konstantinos Votis & Dimitrios Tzovaras, 2025. "Principal Component Random Forest for Passenger Demand Forecasting in Cooperative, Connected, and Automated Mobility," Sustainability, MDPI, vol. 17(6), pages 1-13, March.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:6:p:2632-:d:1613733
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    References listed on IDEAS

    as
    1. Rui Xue & Daniel (Jian) Sun & Shukai Chen, 2015. "Short-Term Bus Passenger Demand Prediction Based on Time Series Model and Interactive Multiple Model Approach," Discrete Dynamics in Nature and Society, Hindawi, vol. 2015, pages 1-11, April.
    2. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    3. Antoniadis, Anestis & Lambert-Lacroix, Sophie & Poggi, Jean-Michel, 2021. "Random forests for global sensitivity analysis: A selective review," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    4. Sofia Polymeni & Vasileios Pitsiavas & Georgios Spanos & Quentin Matthewson & Antonios Lalas & Konstantinos Votis & Dimitrios Tzovaras, 2024. "Toward Sustainable Mobility: AI-Enabled Automated Refueling for Fuel Cell Electric Vehicles," Energies, MDPI, vol. 17(17), pages 1-17, August.
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