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A Comprehensive Overview of Deep Learning for Algorithmic Pricing in Ride-Sharing Platforms

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  • Mioara Chirita

    (Dunarea de Jos University of Galati, Romania)

  • George Chirita

    (Dunarea de Jos University of Galati, Romania)

Abstract

This study extends the current scholarship on algorithmic pricing within the sharing economy. By leveraging the capabilities of deep learning, we seek to generate valuable knowledge for stakeholders in the ride-sharing domain, including platform operators, users, and policymakers. This research contributes to the field of economic science by demonstrating the potential application of deep learning in algorithmic pricing models for sharing economy platforms. Through a comparative analysis of various methodologies, we aim to provide actionable insights that can inform platform design, regulatory frameworks, and ultimately lead to a more efficient, equitable, and sustainable transportation system.

Suggested Citation

  • Mioara Chirita & George Chirita, 2024. "A Comprehensive Overview of Deep Learning for Algorithmic Pricing in Ride-Sharing Platforms," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 1, pages 177-181.
    • Handle: RePEc:ddj:fseeai:y:2024:i:1:p:177-181
    DOI: 10.35219/eai15840409404
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    References listed on IDEAS

    as
    1. Chiwei Yan & Helin Zhu & Nikita Korolko & Dawn Woodard, 2020. "Dynamic pricing and matching in ride‐hailing platforms," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(8), pages 705-724, December.
    2. Sai Wang & Jianjun Wang & Chicheng Ma & Dongyi Li & Lu Cai, 2024. "The Real-Time Dynamic Prediction of Optimal Taxi Cruising Area Based on Deep Learning," Sustainability, MDPI, vol. 16(2), pages 1-23, January.
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