Link to Report: Coming Soon
Background :
This project aims to develop a novel theoretical framework and a practical decision-support tool to guide strategic bridge toll implementation under real-world behavioral and operational constraints. Traditional toll optimization and project evaluation models focus on market uncertainties but neglect critical human behavioral factors, such as present bias, that significantly influence decision outcomes. To bridge this gap, the proposed research introduces an optimization framework that integrates behavioral dynamics into infrastructure decision-making, enabling the identification of strategies that maximize long-term social welfare while addressing short-term user response and implementation pressures. The accompanying decision-support tool will translate this framework into an interactive, user-friendly platform for transportation agencies and policymakers. It will allow users to simulate and compare alternative tolling strategies, assess implementation timelines, and visualize trade-offs between system efficiency, user response, and long-term performance outcomes. By empowering decision-makers to make data-driven, welfare-maximizing choices, this project supports more effective, publicly acceptable, and operationally robust tolling practices. The research will generate theoretical advances, peer-reviewed publications, and an actionable tool ready for integration with FDOT’s planning processes, ultimately contributing to more resilient, safe and efficient transportation infrastructure systems.
Objectives :
This project seeks to establish a new paradigm for strategic bridge toll implementation by integrating behavioral dynamics into traditional optimization frameworks. The objectives are threefold:
- Develop a novel optimization framework that explicitly incorporates present bias into the timing and pricing decisions for bridge toll implementation. The framework will quantify how these behavioral and institutional factors alter the optimal strategy that maximizes long-term social welfare under uncertainty.
- Design and prototype a decision-support system (DSS) that operationalizes the theoretical model into a user-friendly tool for policymakers and transportation agencies. The DSS will enable users to simulate alternative tolling strategies, visualize welfare trade-offs, and assess implementation risks under varying behavioral conditions.
- Validate and disseminate the framework and tool through numerical experiments, peer-reviewed publications, and conference presentations. The validation will demonstrate the social welfare gains of optimized timing decisions, while dissemination will foster adoption by transportation agencies and bridge management programs.
Scope :
Task 1: Development of optimization framework that integrates present bias into bridge toll implementation decision
The first task will focus on developing the core theoretical foundation of the project: a novel optimization framework that integrates present bias into bridge toll implementation decisions. The research team will begin by conducting a comprehensive review of the existing literature on toll pricing optimization, behavioral economics (specifically time-inconsistent preferences and quasi-hyperbolic discounting). This review will ensure that the model formulation builds upon the most relevant theories while addressing the critical gaps identified in current research.
Following this review, the project will formulate a stochastic optimization model that explicitly incorporates behavioral factors into the decision-making process. The model will represent present bias through a quasi-hyperbolic discounting framework, capturing how policymakers and road users and the public tend to overweight immediate costs relative to long-term benefits. Funding cycles will be incorporated as periodic constraints that simulate election-related decision delays and policy timing trade-offs. The objective function will maximize long-term social welfare by balancing economic efficiency, financial feasibility, and behavioral realism. Sensitivity analyses will be conducted to explore how changes in behavioral parameters (e.g., discount rates, bias coefficients) influence optimal toll implementation timing and outcomes.
By the end of this phase, the research team will produce a comprehensive technical report detailing the model’s mathematical formulation, assumptions, and theoretical implications. Preliminary findings will be prepared for submission to a peer-reviewed transportation economics or policy journal. This phase establishes the scientific foundation upon which the practical tool and validation activities will be built.
Task 2: Development and Operationalization of the Decision-Support System (DSS)
The second phase will translate the theoretical model developed in Phase I into a functional, user-friendly decision-support system (DSS) designed for use by policymakers, planners, and transportation agencies. This phase emphasizes the operationalization of theory into practice, ensuring that the model’s complexity is accessible through an intuitive digital interface. The DSS will allow users to input relevant parameters, such as traffic demand, toll structure, project costs, election cycles, and behavioral bias coefficients, and simulate various implementation scenarios.
The development process will begin with the design of computational algorithms capable of solving the optimization model efficiently under multiple behavioral configurations. The research team will build algorithmic modules for computing welfare-maximizing strategies, running Monte Carlo simulations, and visualizing the effects of political and behavioral variables. Parallel to the computational development, a graphical user interface (GUI) will be designed to facilitate easy interaction, enabling non-technical users to explore results without advanced modeling knowledge. The GUI will feature data visualization components such as welfare trade-off graphs, implementation timelines, and sensitivity plots.
To ensure practical relevance, the DSS will be developed using sample data derived from Florida transportation contexts, such as bridge tolling scenarios on the Turnpike or the I-4 corridor. The team will conduct iterative internal testing to evaluate performance, stability, and usability. Input from advisory panel members and FDOT collaborators will be solicited to refine tool functionality and ensure that it aligns with real-world decision-making needs. Deliverables from this phase will include a working prototype of the decision-support tool, accompanied by a user manual and an interim technical report summarizing the system architecture and pilot results.
Task 3: Model Validation, Evaluation, and Dissemination
The final phase will focus on validating the proposed framework and decision-support tool through empirical analysis, stakeholder engagement, and dissemination of results. Validation will be conducted through numerical case studies using data from Florida bridge infrastructure projects. The model will be calibrated using realistic traffic demand profiles, cost structures, and election-cycle parameters to test the robustness and applicability of the DSS. Scenario analyses will be performed to compare welfare outcomes under different behavioral biases, political cycle lengths, and tolling timelines. These simulations will quantify the social welfare gains and policy efficiency improvements achievable through optimized decision timing.
The evaluation process will include structured feedback from stakeholders, including FDOT officials, academic experts, and members of the Research Advisory Panel. Their input will help refine the DSS to ensure it meets the practical requirements of transportation agencies and integrates seamlessly with existing FDOT and ABC-UTC planning workflows. Based on these results, the research team will finalize the decision-support system and produce comprehensive implementation guidelines detailing best practices for bridge toll policy design under behavioral and political constraints.
Dissemination activities will include the preparation of peer-reviewed journal papers and conference presentations to share key findings with the broader transportation research community. The team will also produce a final project report summarizing theoretical advancements, tool design, validation results, and recommendations for adoption by agencies. This final phase will ensure that the project’s intellectual contributions and practical outputs are widely accessible, establishing a strong foundation for future funding proposals to FDOT, NSF, and other agencies.
Research Team :
Principal Investigator: Qianwen (Vivian) Guo
Co-Principal Investigator: Eren Erman Ozguven