Brar, Avalpreet Singh (Nanyang Technological University), Su, Rong (Nanyang Technological University), Kaur, Jaskaranveer (Continental Automotive), Li, Xinling (Massachusetts Institute of Technology), Zardini, Gioele (Massachusetts Institute of Technology)

Maximal Compatibility Matching for Preference-Aware Ride-Hailing Systems

Scheduled for presentation during the Regular Session "S36b-Behavior Modeling and Decision-Making in Traffic Systems" (FR-EA-T36), Friday, November 21, 2025, 14:30−14:50, Surfers Paradise 3

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

This paper presents the Maximal Compatibility Matching (MCM) framework, a novel assignment strategy for ride-hailing systems that explicitly incorporates passenger comfort into the matching process. Traditional assignment methods prioritize spatial efficiency, but often overlook behavioral alignment between passengers and drivers, which can significantly impact user satisfaction. MCM addresses this gap by learning personalized passenger comfort zones using gradient-boosted decision tree classifiers trained on labeled ride data, and by modeling driver behavior through empirical operating profiles constructed from time-series driving features. Compatibility between a passenger and a driver is computed as the closed form volume of intersection between their respective feature space regions. These compatibility scores are integrated into a utility-based matching algorithm that balances comfort and proximity through a tunable trade-off parameter. We validate the framework using a Unity-based driving simulator with real-time passenger feedback, demonstrating that MCM enables more personalized and socially acceptable matchings while maintaining high levels of operational performance.