FEDORA Scientific Publication: Assessing the impacts of tradable credit schemes through agent-based simulation

Researchers within the Fedora Project have published a new paper titled “Assessing the impacts of tradable credit schemes through agent-based simulation” in Journal of Intelligent Transportation Systems -Technology, Planning, and Operations. The publication explores a fully decentralized, model-free, and infrastructure-free approach to variable speed limit control—V2VSLs. The paper was authored by Renming Liua, Dimitrios Argyrosa, Yu Jianga, Moshe E. Ben-Akivab, Ravi Seshadria, and Carlos Lima Azevedo.

Highlights

  • Tradable credits replace road tolls: travelers receive credits, spend them for peak-hour travel, and can buy or sell extras.
  • Realistic simulation: researchers modeled thousands of travelers, their daily trips, route choices, and credit-market decisions simultaneously.
  • Less congestion: peak-hour credit charges encouraged travelers to shift departure times, reducing traffic buildup and improving travel speeds.
  • Stable market outcomes: credit prices and trading activity gradually stabilized, matching theoretical expectations for a functioning credit market.
  • Reduced market gaming: a minimum-profit rule cut unnecessary buying and selling while preserving most congestion and welfare benefits

Abstract

Tradable credit schemes (TCS) are an alternative to congestion pricing, offering revenue neutrality and the potential to address equity concerns through the credit allocation. Past research on the performance of TCS has largely relied on simplified network and market equilibrium models that may fail to capture the complexities of transportation demand, supply, and credit market interactions. Agent- and activity-based simulation provides a more comprehensive approach by explicitly modeling individual traveler behaviors and market dynamics. This study proposes an integrated simulation framework for TCS implementation within the open-source urban simulation platform SimMobility, featuring: (a) a flexible TCS design that accounts for multiple trips and individual trading behaviors; (b) a simulation framework that models interactions between travelers, the TCS regulator, and the market; (c) TCS optimized using Gaussian Processes and Bayesian Optimization, and (d) simulation experiments on a large-scale mesoscopic multimodal network. Results show that network and market performance stabilize over time, aligning with theoretical TCS properties from network equilibrium models. We confirm the efficiency of TCS in reducing congestion and explore its varied impacts on users, travel behavior, and market dynamics. Our framework allows for designing different TCS configurations and testing their effect in mitigating potentially undesirable trading and market behavior, ultimately contributing to a closer-to-practice design and assessment.

 
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FEDORA Scientific Publication: V2VSL: Infrastructure-Free, Decentralized Variable Speed Limit Control

 

Researchers within the Fedora Project have published a new paper titled “V2VSL: Infrastructure-Free, Decentralized Variable Speed Limit Control” in Data Sciece for Transportation. The publication explores a fully decentralized, model-free, and infrastructure-free approach to variable speed limit control—V2VSLs. The paper was authored by Kevin Riehl, Davide Pusino, Anastasios Kouvelas, and Michail A. Makridis.

 

Highlights

  • Assesses performance on  three highway bottleneck scenarios examples.
  • The proposed method achieves significant improvements in traffic states, with up to 15% higher speeds, 5% lower density, and 8% higher flows.
  • V2VSL achieves efficiency gains comparable to centralised VSL systems without the need for similar infrastructure, detailed models, and centralised communication.

Abstract

Traffic congestion is a pertinent issue on highways, with severe consequences on environment, economy, and quality of life. Variable speed limit control can help avoid traffic jams before congestion forms, as vehicles upstream are required to decelerate at times to stop emerging congestion from propagating and expanding. This work proposes a fully decentralized, model-free, and infrastructure-free approach to variable speed limit control—V2VSL—that employs connected vehicles as communication infrastructure, as moving sensors, and as actuators. Dedicated short range communication, consensus algorithm and gossip algorithm protocols, and a Bellman controller are components of this approach. At the example of three highway bottleneck scenarios, performance is assessed by traffic micro-simulations, that show the approach is robust to gaps between platoons and capable of recovering from periods of disconnection. The proposed method achieves significant improvements in traffic states, with up to 15% higher speeds, 5% lower density, and 8% higher flows. These traffic improvements become significant at a compliance rate as low as 25%, making the method potentially viable in near-term mixed traffic environments with partial CAV penetration. V2VSL achieves efficiency gains comparable to centralized VSL systems, but without requiring roadside infrastructure, detailed traffic models, or centralized communication. An open-source implementation and computational results are provided as SUMO simulation with Python on GitHub: https://github.com/DerKevinRiehl/decentralized_vsl/ . 

 

Read the full paper here