Researchers Zenghao Hou and Ludovic Leclercq, affiliated with the University of Paris-Est, have developed a novel framework to optimize the integration of Tradable Credit Schemes (TCS) and Demand-Responsive Autonomous Shuttles (DRAS) to encourage a shift from private car use to public and shared transport. Their work, published in the journal Transportation Research Part B, addresses key limitations in existing models and provides practical insights for energy and transportation sectors.
Tradable Credit Schemes are designed to limit private car usage by capping the number of miles or kilometers that can be driven, while allowing individuals to trade credits to maintain fair outcomes. However, many existing studies assume unlimited public transit capacity or fixed occupancy rates for shared modes, often overlooking waiting times and oversimplifying time-based costs. These oversights can lead to an overestimation of the system’s performance, particularly when public or shared transport supplies are insufficient.
To address these gaps, Hou and Leclercq developed a dynamic multimodal equilibrium model that captures operational constraints and induced waiting times under TCS regulation. The model integrates travelers’ mode choices, credit trading, traffic dynamics, and waiting time, taking into account key operational features of service vehicles such as fleet size and capacity. This comprehensive approach provides a more accurate representation of real-world conditions and system performance.
Furthermore, the researchers proposed a bi-level optimization framework that combines the equilibrium model with adaptive supply management through the deployment of Demand-Responsive Autonomous Shuttles (DRAS). This framework allows for the joint optimization of TCS design and operational strategies for DRAS, enabling a more flexible and responsive transport system.
To demonstrate the effectiveness of their framework, the researchers applied it to a section of the A10 highway near Paris, France. The numerical results highlighted the importance of modeling operational features within multimodal equilibrium and incorporating flexible supply in TCS policies. The findings showed that jointly implementing TCS and DRAS can significantly mitigate overall generalized cost, which includes factors such as travel time, waiting time, and monetary expenses.
For the energy sector, this research offers valuable insights into optimizing transport systems to reduce private car usage, which can lead to lower energy consumption and emissions. By promoting the use of public and shared transport, particularly through the integration of autonomous shuttles, energy demand can be more effectively managed and potentially reduced. Additionally, the dynamic modeling approach can help energy planners and policymakers better understand the interplay between transport demand and supply, enabling more informed decision-making and strategic planning.
In summary, the work of Zenghao Hou and Ludovic Leclercq presents a robust framework for fostering modal shift through the integration of Tradable Credit Schemes and Demand-Responsive Autonomous Shuttles. Their research provides practical applications for the energy and transportation sectors, offering a pathway to more efficient, sustainable, and responsive transport systems.
This article is based on research available at arXiv.