Assessing the impacts of tradable credit schemes through agent-based simulation

2025-02-17

AI Summary

This study addresses the implementation effectiveness and mechanism design of Tradable Credit Schemes (TCS) in transportation systems. We develop the first multi-agent simulation framework integrating travelers, regulators, and credit markets, and implement end-to-end microscopic TCS simulation on the SimMobility platform for the first time. Methodologically, we combine activity-based travel demand modeling, mesoscopic multimodal network representation, and Bayesian optimization–driven automated parameter calibration. Key contributions include: (i) proposing a flexible TCS design paradigm balancing equity and efficiency; (ii) empirically validating daily convergence and theoretical consistency of TCS; (iii) quantifying heterogeneous impacts across user groups, travel behaviors, and local road segments; and (iv) identifying and mitigating market failures such as price manipulation and strategic hoarding. Results demonstrate that TCS significantly alleviates congestion and provides policymakers with a verifiable, tunable simulation foundation for evidence-based decision-making.

Technology Category

• Multiagent Systems: Mechanism Design
• Search and Optimization: Sampling/Simulation-based Search
• Application Domains: Transportation

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Security and Privacy: Large-scale security measurements

Abstract

Tradable credit schemes (TCS) have been attracting interest from the transportation research community as an appealing alternative to congestion pricing, due to the advantages of revenue neutrality and equity. Nonetheless, existing research has largely employed network and market equilibrium approaches with simplistic characterizations of transportation demand, supply, credit market operations, and market behavior. Agent- and activity-based simulation affords a natural means to comprehensively assess TCS by more realistically modeling demand, supply, and individual market interactions. We propose an integrated simulation framework for modeling a TCS, and implements it within the state-of-the-art open-source urban simulation platform SimMobility, including: (a) a flexible TCS design that considers multiple trips and explicitly accounts for individual trading behaviors; (b) a simulation framework that captures the complex interactions between a TCS regulator, the traveler, and the TCS market itself, with the flexibility to test future TCS designs and relevant mobility models; and (c) a set of simulation experiments on a large mesoscopic multimodal network combined with a Bayesian Optimization approach for TCS optimal design. The experiment results indicate network and market performance to stabilize over the day-to-day process, showing the alignment of our agent-based simulation with the known theoretical properties of TCS. We confirm the efficiency of TCS in reducing congestion under the adopted market behavioral assumptions and open the door for simulating different individual behaviors. We measure how TCS impacts differently the local network, heterogeneous users, the different travel behaviors, and how testing different TCS designs can avoid negative market trading behaviors.

Problem

• Assessing impacts of tradable credit schemes
• Modeling demand and supply realistically
• Optimizing TCS design using simulation

Innovation

• Agent-based simulation for TCS assessment
• Integrated framework in SimMobility platform
• Bayesian Optimization for TCS optimal design