Assignment and Pricing of Shared Rides in Ride-Sourcing using Combinatorial Double Auctions (1909.08608v3)

Abstract: Transportation Network Companies employ dynamic pricing methods at periods of peak travel to incentivise driver participation and balance supply and demand for rides. Surge pricing multipliers are commonly used and are applied following demand and estimates of customer and driver trip valuations. Combinatorial double auctions have been identified as a suitable alternative, as they can achieve maximum social welfare in the allocation by relying on customers and drivers stating their valuations. A shortcoming of current models, however, is that they fail to account for the effects of trip detours that take place in shared trips and their impact on the accuracy of pricing estimates. To resolve this, we formulate a new shared-ride assignment and pricing algorithm using combinatorial double auctions. We demonstrate that this model is reduced to a maximum weighted independent set model, which is known to be APX-hard. A fast local search heuristic is also presented, which is capable of producing results that lie within 10% of the exact approach for practical implementations. Our proposed algorithm could be used as a fast and reliable assignment and pricing mechanism of ride-sharing requests to vehicles during peak travel times.

• The paper introduces a novel shared ride pricing algorithm using combinatorial double auctions that integrates detour effects to enhance social welfare.
• It reformulates the ride assignment problem as a maximum weighted independent set, addressing the computational challenges in large-scale, real-time applications.
• The authors design a fast local search heuristic that achieves outcomes within 10% of the exact solution, ensuring practical use during peak demand.

The paper "Assignment and Pricing of Shared Rides in Ride-Sourcing using Combinatorial Double Auctions" addresses the problem of dynamic pricing and assignment in ride-sourcing platforms such as Uber and Lyft, particularly focusing on shared rides. Transportation Network Companies (TNCs) often employ surge pricing to balance rider demand and driver availability during peak times, but this traditional approach does not adequately consider the complexities of shared trip detours and their impact on pricing accuracy.

The authors propose a novel shared-ride assignment and pricing algorithm that utilizes combinatorial double auctions. In this context, both customers and drivers submit their valuations for different ride options, allowing for a market-driven approach to assignment and pricing that theoretically maximizes social welfare. One of the key innovations of the proposed method is its ability to incorporate detour effects into the pricing model, a notable limitation in existing dynamic pricing systems.

The framework formulated by the authors can be reduced to a maximum weighted independent set problem, which is known to be APX-hard, indicating that finding an exact solution is computationally infeasible for large-scale real-time applications. To this end, the authors introduce a fast local search heuristic that efficiently approximates the optimal solution. Empirical results reported in the paper suggest that this heuristic performs remarkably well, achieving outcomes within 10% of the exact solution, making it a viable method for practical implementation during peak travel times.

In summary, the proposed combinatorial double auction mechanism for shared-ride assignment and pricing marks an advancement in ride-sourcing strategies by addressing the detour effects in shared trips. This approach promises enhanced social welfare through a more accurate and responsive pricing model, ultimately benefiting both riders and drivers during periods of high demand.