JOURNAL ARTICLE

Sharing the Shared Rides: Multi-Party Carpooling Supported Strategy-Proof Double Auctions.

  • Published In: Production & Operations Management, 2024, v. 33, n. 7. P. 1569 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Yu, Hao; Huang, Min; Yue, Xiaohang 3 of 3

Abstract

This article focuses on the design of strategy-proof double auction mechanisms to support multi-party carpooling, where multiple riders with overlapping itineraries share rides from a single driver. It introduces the MASTER (Multi-party cArpooling SupporTed stratEgy-pRoof) family of mechanisms tailored for both scheduled and on-demand carpooling scenarios, addressing challenges such as personalized carpooling constraints, information asymmetry, and strategic bidding. The scheduled mechanisms (MASTERmS and MASTERMS) differ in how group bids are determined and include filtration processes to maintain individual rationality, while the on-demand mechanisms (MASTERO) incorporate frustration-based promotion to prioritize riders with longer waits and provide operational flexibility via adjustable promotion strength. Experimental results based on real-world data demonstrate that multi-party carpooling improves social welfare and car occupancy rates, with the proposed mechanisms outperforming existing academic and practical counterparts in allocation efficiency and service responsiveness, especially under varying market conditions and rider-driver ratios.

Additional Information

  • Source:Production & Operations Management. 2024/07, Vol. 33, Issue 7, p1569
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2024
  • ISSN:1059-1478
  • DOI:10.1177/10591478241252746
  • Accession Number:179238340
  • Copyright Statement:Copyright of Production & Operations Management is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.