The objectives of this project are to quantify the relationship between criticality and travel time reliability (TTR) in transportation networks to assess if one can serve as a proxy or an auxiliary measure for the other and to facilitate informed decision-making for transportation investments. In essence, the TTR and criticality topics are research fields of their own with distinct analysis approaches and metrics, even though both fields have commonalities starting with the aim of assessing the transportation network performance.

This project constitutes a research effort to verify and calibrate a Dynamic Toll Lane (DTL) microsimulation model (Phase I) that will be used to test innovative pricing and control strategies aimed at reducing congestion (Phase II), specifically in a DTL and the main tollway lanes in the Metropolitan Area of San Juan, PR. The DTL was built recently and has already experienced congestion at the entrances and exits. The primary objective of this facility is to reduce, in some way, the time it takes to traverse the congested section of the freeway during peak hours.

Urban stormwater flooding represents a persistent and escalating challenge for transportation networks and the well-being of urban populations, causing significant disruptions, economic losses, and safety hazards. This research project directly addresses this critical issue by advancing the development and validation of empirical models capable of predicting the depth and velocity of floodwaters in urban environments under a range of precipitation scenarios.

This project focuses on developing a robust and versatile traffic network model for analyzing a wide range of transportation policies in New York City, particularly in Northern Manhattan. While the initial impetus for this project stemmed from concerns about the potential impacts of congestion pricing on neighborhoods adjacent to the Manhattan Central Business District (CBD), the model's design will allow for the evaluation of diverse transportation strategies beyond just pricing schemes.

Within the dynamic field of transportation research, Reinforcement Learning (RL) has been recognized as a critical approach to monitor, model, and manage transportation systems. Among the diverse array of RL techniques, the Upper Confidence Bound (UCB) algorithm stands out for its potential in solving long-standing transportation problems.