On-Trip Matching and Pricing for Shared Rides
Yifan Shen, Junlin Chen, Julia Yan, Chiwei Yan

TL;DR
This paper introduces a dynamic, stochastic matching model for shared rides that considers both pre-trip and on-trip matching, demonstrating improved efficiency and profitability especially in sparse outskirts.
Contribution
It develops a novel matching framework that explicitly models on-trip matching decisions and integrates them with pricing optimization, addressing limitations of existing batch models.
Findings
On-trip matching enhances profitability and efficiency.
Pre-trip matching suits dense urban areas.
On-trip matching is crucial in sparse outskirts.
Abstract
Although shared rides have the potential to increase vehicle utilization and reduce congestion and emissions, these benefits depend heavily on ridesharing platforms' ability to match riders effectively. As such, shared rides have seen limited success outside of dense urban areas -- the sparse outskirts of greater metropolitan areas remain underserved. In the literature, the dominant matching model involves collecting rider requests in a batch interval and solving a non-bipartite matching problem on the requests. However, this model neglects the ability of a rider to be matched to a future arriving rider even after she is initially dispatched solo; namely, matching is only modeled pre-trip, and the value of on-trip matching is not explicitly accounted for. We develop a dynamic, stochastic matching model, where the platform makes both pre-trip and on-trip matching decisions, and contrast…
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