Modeling Morning Commute Problem with Real-Time Ridesharing Services

The development of on-demand ridesharing platforms has reshaped commuters’ travel behavior, enabling commuters to decide whether to become ridesharing drivers or passengers in real-time. To examine the impact of commuters’ decisions and ridesharing platforms’ strategies on the equilibrium state in the morning commute problem, this paper formulates an equilibrium framework to characterize commuters’ mode choice and travel time choice behavior based on the bottleneck model. The time-varying mismatch problem and commuters’ heterogeneous preference for ridesharing mode are considered. Analytical sensitivity analysis is conducted to discuss how market share of ridesharing mode changes with the platform’s pricing strategies based on equilibrium state. Optimal strategies for profit and market share maximization are also studied. We find that supply of ridesharing drivers must be coordinated with passenger demand when the optimal state is achieved. Results are further demonstrated to verify the analytical findings. Our analyses provide insights for pricing strategies of ridesharing platforms.