Vehicle Rebalancing Under Adherence Uncertainty

TL;DR

This paper introduces an adherence-aware vehicle rebalancing framework that models driver behavior uncertainties, significantly improving ride-hailing system efficiency and profitability through a novel optimization approach and real-time implementation.

Contribution

It presents the first integrated model addressing driver adherence uncertainty in vehicle rebalancing, combining demand forecasting, driver preferences, and confidence tracking into a real-time optimization framework.

Findings

AAVR outperforms baseline methods with 28% more served demand.

Customer wait times reduced by 22.7%.

Platform earnings increased by 28.6%.

Abstract

Ride-hailing systems often suffer from spatiotemporal supply-demand imbalances, largely due to the independent and uncoordinated actions of drivers. While existing fleet rebalancing methods offer repositioning recommendations to idle drivers to improve service efficiency, they typically assume full driver compliance: an unrealistic premise in practice. We propose an Adherence-Aware Vehicle Rebalancing (AAVR) framework that explicitly models and addresses uncertainties in driver adherence, stemming from individual behavioral preferences and dynamic trust in the recommender system. Our approach integrates (i) region-specific XGBoost models for demand forecasting, (ii) a network-level XGBoost model for inter-region travel time prediction, (iii) driver-specific logit models to capture repositioning preferences, and (iv) driver-specific Beta-Bernoulli Bandit models with Thompson Sampling to track and update each driver's confidence in the system over time.These elements are incorporated into a novel optimization framework that generates adherence-aware repositioning recommendations. To enable real-time implementation, we further develop a linearized version of the AAVR model. Extensive simulations on the NYC taxi dataset demonstrate that AAVR significantly outperforms four state-of-the-art adherence-agnostic baselines, achieving a 28% increase in served demand, a 22.7% reduction in customer wait times, a 28.6% increase in platform earnings and a 26% gain in driver profits on average.