Wise Goose Chase: A Predictive Path Planning Algorithm for Dynamic Rebalancing in Ride-Hailing Systems

TL;DR

The paper introduces Wise Goose Chase, a predictive path planning algorithm for ride-hailing rebalancing that anticipates future demand and supply, improving efficiency over traditional destination-based methods.

Contribution

It presents a novel event-triggered, driver-specific path planning framework using RFDEs to forecast demand-supply dynamics and optimize driver routes in real-time.

Findings

Outperforms baseline rebalancing strategies in simulations.

Effectively models supply and demand evolution with RFDEs.

Enhances ride-hailing efficiency through predictive, context-aware rebalancing.

Abstract

Traditional rebalancing methods in ride-hailing systems direct idle drivers to fixed destinations, overlooking the fact that ride allocations frequently occur while cruising. This destination-centric view fails to exploit the path-dependent nature of modern platforms, where real-time matching depends on the entire trajectory rather than a static endpoint. We propose the Wise Goose Chase (WGC) algorithm, an event-triggered, driver-specific path planning framework that anticipates future matching opportunities by forecasting spatio-temporal supply and demand dynamics. WGC uses a system of Retarded Functional Differential Equations (RFDEs) to model the evolution of idle driver density and passenger queues at the road-segment level, incorporating both en-route matching and competition among drivers. Upon request, WGC computes personalized cruising paths that minimize each driver's expected time to allocation. Monte Carlo simulations on synthetic urban networks show that WGC consistently outperforms baseline strategies, highlighting the advantage of predictive, context-aware rebalancing in dynamic mobility systems.