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Determinants of On-Street Parking Demand: A Machine Learning Approach in the City of Lyon
[Déterminants de la demande de stationnement sur rue : Une approche par Machine Learning sur la ville de Lyon]

Author

Listed:
  • Edith Combes

    (LAET - Laboratoire Aménagement Économie Transports - UL2 - Université Lumière - Lyon 2 - ENTPE - École Nationale des Travaux Publics de l'État - CNRS - Centre National de la Recherche Scientifique, ENTPE - École Nationale des Travaux Publics de l'État - ENTPE - École Nationale des Travaux Publics de l'État - Ministère de l'Ecologie, du Développement Durable, des Transports et du Logement)

Abstract

Parking pricing is a central tool for urban access regulation policies. To assess the effectiveness of these measures, it is important to understand the determinants of parking demand, especially in the context of innovations in parking policy. This report explores the key issues related to urban parking, followed by a review of the literature on the determinants of parking choices. We then present the various databases used for this study. A supervised machine learning approach, the XgBoost algorithm, is applied to on-street parking payment transaction data from the city of Lyon to identify the spatial and temporal factors influencing parking duration. The modeling results will enhance understanding of the determinants of parking behavior, ultimately facilitating the evaluation of the new progressive pricing policy implemented by the city of Lyon in June 2024.

Suggested Citation

  • Edith Combes, 2024. "Determinants of On-Street Parking Demand: A Machine Learning Approach in the City of Lyon [Déterminants de la demande de stationnement sur rue : Une approche par Machine Learning sur la ville de Ly," Working Papers hal-05201274, HAL.
    • Handle: RePEc:hal:wpaper:hal-05201274
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