Avalpreet Singh Brar

Avalpreet Singh Brar, Rong Su, Christos G. Cassandras et al.

Ride-hailing platforms frequently face spatiotemporal supply-demand imbalances caused by uneven passenger demand and decentralized driver decision-making. Existing vehicle rebalancing methods typically assume drivers always follow repositioning recommendations or model adherence using static probabilities. In practice, adherence evolves through repeated interactions with the platform. We propose the Adherence-Aware Vehicle Rebalancing (AAVR) model, which generates simultaneous fleet-wide repositioning recommendations while explicitly accounting for driver preferences and dynamically evolving adherence. The resulting optimization problem is computationally intractable, so we derive a tractable upper-bound reformulation that enables real-time recommendation generation for large-scale systems. Simulations on the NYC taxi dataset under dynamic adherence updates show that AAVR consistently outperforms state-of-the-art methods, improving served demand by 26.72%, reducing passenger waiting time by 26.45%, increasing platform and driver profits by 25.90% and 28.75%, respectively, and improving fleet adherence by 30.06%. These results demonstrate that modeling evolving driver adherence improves both operational performance and long-term adherence to platform recommendations.

Avalpreet Singh Brar, Rong Su, Jaskaranveer Kaur et al.

Human driver participation is a critical source of uncertainty in Mobility-on-Demand (MoD) rebalancing. Drivers follow platform recommendations probabilistically, and their willingness to comply evolves with experienced outcomes. This creates a closed-loop feedback in which stronger recommendations increase participation, participation increases congestion, congestion lowers allocation success, and realized allocations update adherence beliefs. We propose a microscopic stochastic model that couples (i) belief-driven participation, (ii) Poisson demand, (iii) uniform matching, and (iv) Beta--Bernoulli belief updates. Under a large-population closure, we derive a deterministic mean-field recursion for the population adherence state under platform actuation. For i.i.d. Poisson demand and constant recommendation intensity, we prove global well-posedness and invariance of the recursion, establish equilibrium existence, provide uniqueness conditions, and show global convergence in the regime where platform recommendations are no weaker than baseline participation. We then define steady-state adherence and throughput, characterize the induced performance frontier, and show that adherence and throughput cannot, in general, be simultaneously maximized under uniform time-invariant actuation. This yields a throughput-maximization problem with an adherence floor. Exploiting the monotone frontier structure, we show the optimal uniform time-invariant policy is the maximal feasible recommendation intensity and provide an efficient bisection-based algorithm.