LAUSR

Freelance drivers in ride-hailing systems may strategically accept or reject ride requests based on their projection of the profitability of the assigned rides. This driver acceptance uncertainty is mainly caused by the flat rate payment and the blind ride acceptance rule adopted by most ride-hailing platforms. As a result, a high driver rejection rate has been observed, causing a negative impact on the service quality and matching efficiency for the ride-hailing systems. In this paper, we propose a pricing mechanism to improve drivers’ average ride acceptance rate by offering personalized payments computed based on the characteristics of individual riders and the estimated acceptance rates of the drivers. Specifically, we model and predict the drivers’ ride acceptance rates through a binary choice model and incorporate it into the stochastic optimization problem for the ride-hailing system. This provides personalized payment for each driver in connection with the characteristics of the assigned ride and the preferences of the drivers. We then evaluate the performance of the proposed pricing mechanism through extensive numerical experiments based on RideAustin trip data from June 2016 to April 2017. The results suggest that our proposed pricing mechanism improves the drivers’ average acceptance rate by an average of 60% compared to some commonly used pricing schemes. It also significantly increases the platform’s expected profit and matching rate. This implies a strong potential for the proposed pricing mechanism to improve service reliability and quality in ride-hailing systems.