Assignment and Pricing of Shared Rides in Ride-Sourcing using Combinatorial Double Auctions

TL;DR

This paper introduces a new combinatorial double auction-based algorithm for assigning and pricing shared rides, accounting for trip detours, and demonstrates its effectiveness with a fast heuristic that performs close to optimal.

Contribution

The paper develops a novel shared-ride assignment and pricing algorithm using combinatorial double auctions that considers trip detours, improving over existing models.

Findings

The model reduces to a maximum weighted independent set problem, which is APX-hard.

A local search heuristic achieves results within 10% of the optimal.

The algorithm provides a fast, reliable mechanism for ride-sharing during peak times.

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.