Ride-sourcing fleet rebalancing with proactive and targeted pricing

This work studies the role of proactive and targeted ride-splitting incentives on the service level and total revenue of a ride-sourcing platform, which is built as a discrete event simulator that incorporates simulated taxi data within a congestible road network. While shared trips offer riders a discount to compensate for any additional travel or waiting time, the success in matching shared trips relies on riders’ attitudes towards the trade-off between travel time and cost. Therefore, formulated as a multinomial logit model, alternative-specific coefficients characterize the probability of a random draw among three options: solo ride, shared ride, or a public-transit-like service. A multi-objective analysis shows that for regions where empty vehicle depletion rate is high, by offering incentives with an additional discount for a shared trip, the platform can proactively rebalance vehicle supply in high-demand regions during peak-hours, and succeed with a small abandonment for trips with high waiting or detour times.