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Team: Siyu Chen, Ravi Seshadri, Carlos Lima Azevedo, Arun Akinnepally, Andrea Araldo, Moshe Ben-Akiva

  • Develop market designs for tradable mobility credit schemes and disaggregate models of individual behavior in the market

  • Design, develop and evaluate a real-time system of TMCs named trinity based on the tenets of prediction, optimization and personalization (Tri-POP)

Tradeable mobility credit (TMC) schemes are an approach to travel demand management that have received significant attention in the transportation domain in recent years. This stream of research aims to contribute to knowledge on TMCs by designing, modeling and evaluating a system of TMCs named trinity based on the fundamental tenets of prediction, optimization and personalization. The methodologies and findings from this study have the potential to accelerate the understanding and real-world deployment of TMCs which will aid transportation agencies and operators in achieving long-term societal goals of sustainability through the mitigation of congestion, reduction of energy and emissions. Algorithms and codes developed will be open-source and integrated with a transportation system prediction and control platform DynaMIT.  

Specifically, the project pursues the following goals: (1) Design and implement a novel bi-level optimization framework tailored for ‘online’ applications that includes two components, a system-level optimization that periodically (in real-time) determines ‘optimal’ token (mobility credit) tariff rates for different mobility options by utilizing short term predictions of the transportation network, and a user-level optimization that will determine a personalized ‘optimal’ menu of mobility options to display to each individual user subject to the optimal system-level token tariff charging policy;  (2) Design and model the operation/dynamics of the token market by considering actions of the user (disaggregate buying and selling decisions and plausible behavioral models incorporating heterogeneity) and regulator, (3) Perform extensive simulation–based experiments on real-world networks to gain insights into how the design of the market/token tariff schemes, user behavior and network conditions can impact performance of the TMC system and market dynamics (for example: how should the allocation/acquisition/expiration of tokens be designed; how and when does the regulator intervene in the market, how should the token tariff policies be designed to allow for scalability/effectiveness of optimization and network control?). The trinity system, in employing complex simulation-based disaggregate transportation and market models will allow us to gain insights into system behavior when the token charges, traffic flow patterns and the market price evolve together.


1. de Palma, A., Proost, S., Seshadri, R., & Ben-Akiva, M. (2018). Congestion tolling-dollars versus tokens: A comparative analysis. Transportation Research Part B: Methodological, 108, 261-280.

2. Liu, R., Chen, S., Jiang, Y., Seshadri, R., Ben-Akiva, M. E., & Azevedo, C. L. (2020). Managing network congestion with tradable credit scheme: a trip-based MFD approach. Under Review. Transportation Research Part C.

3. Chen, S., Seshadri, R., Azevedo, C. L., Akkinepally, A. P., Liu, R., Araldo, A., ... & Ben-Akiva, M. E. (2021). Analysis and Design of Markets for Tradable Mobility Credit Schemes. arXiv preprint arXiv:2101.00669.

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