hsueh, Cheng-Fen (National Cheng Kung University), Hu, Ta-Yin (National Cheng Kung University)

Multi-Objective Multi-Agent Deep Reinforcement Learning for Dynamic Pricing and RideSharing Optimization

Scheduled for presentation during the Regular Session "S35b-Optimization, Control, and Learning for Efficient and Resilient ITS" (FR-EA-T35), Friday, November 21, 2025, 13:30−13:50, Surfers Paradise 2

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 an integrated framework for autonomous mobility-on-demand (AMoD) systems, focusing on dynamic pricing, ride-sharing, and decentralized coordination. Built on a high-fidelity, city-scale environment calibrated with NYC taxi data, the framework dynamically generates passenger and shared autonomous vehicle (SAV) agents based on real-world spatiotemporal demand patterns. The system integrates a multi-objective multi-agent deep reinforcement learning (MO-MADRL) framework with centralized training and decentralized execution (CTDE), allowing agents to optimize individual incentives and system-level social welfare jointly. Adaptive pricing strategies, flexible ride-matching mechanisms, and zone-based geographic abstractions are included to enhance computational efficiency while maintaining geographic realism. Experimental results demonstrate that our framework consistently improves key performance indicators like passenger waiting times, vehicle utilization, and pricing stability, outperforming purely centralized or decentralized methods. This research provides a robust platform for testing adaptive policies and advancing scalable, equitable AMoD system design.