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Modeling and Simulation

Relationship Between Travel Time Reliability and Criticality in Transportation Networks: A Neural Networks Approach

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.

Phase II of Dynamic Toll Lane Microsimulation Calibration and Simulation: Improving Mobility by developing new pricing and control strategies for Dynamic Toll Lanes

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.

Enhancing and validating models for predicting urban stormwater flooding

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.

Simulating Congestion Pricing Effects on Traffic Patterns in Neighboring Communities

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.

Reinforcement Learning Methods for Traffic Demand Analysis and Control in Intelligent Transportation Systems

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.

Dynamic Toll Lane Microsimulation Calibration and Simulation: Improving Mobility by developing new pricing and control strategies for Dynamic Toll Lanes

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.

Feasibility of employee shuttles for equitable mobility and improved housing options for low- and middle-income employees: A Case for Stony Brook University Campus

The objective of this project is to assess the feasibility of an employee shuttle for Stony Brook University (SBU) campus employees to reduce car dependency and to expand employee access to more affordable housing choices. The ultimate aim of the project is to develop a demand responsive employee shuttle pilot through an online mobility platform for work-home commute, complemented by on-demand service for noncommute trips (e.g., grocery) and carpool matching.

Transportation Risk and Resilience Metrics

This research, addressing the areas of Inclusive Advanced Technology Application and Climate Resilient Infrastructure, will evaluate a set of proof-of-concept transportation resilience measures to determine their utility and scalability as state and local performance measures. The research will review the latest scientific literature on risk and resilience measures to catalog methodologies scoring road network assets based on road segment attributes, hazard intersections, network centrality, and accessibility.

Evaluating a Microhub Pilot Program

Rapid urbanization in cities like New York City (NYC) has spurred an overwhelming surge in consumer demand, with a consequential 80% of deliveries now aimed at residential customers. Predominantly facilitated by trucks, which account for 90% of deliveries, this has detrimentally impacted air quality, traffic congestion, and overall life quality. In response, NYC has initiated the concept of micro-distribution centers or microhubs—spaces designed to transition deliveries from larger trucks to sustainable modes such as electric vehicles or cargo cycles.

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