Brar, Avalpreet Singh (Nanyang Technological University), Su, Rong (Nanyang Technological University), Cassandras, Christos (Boston University), Zardini, Gioele (Massachusetts Institute of Technology)

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

Scheduled for presentation during the Regular Session "S34b-Data-Driven Optimization and Governance in Intelligent Urban Mobility" (FR-EA-T34), Friday, November 21, 2025, 14:50−15:30, Surfers Paradise 1

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

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 spatiotemporal 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.