Blockchain technology, predominantly utilized within cryptocurrency, is being increasingly adapted across diverse sectors, and transportation systems is not an exception. Despite presenting several challenges, blockchain technology also offers various advantages. Understanding Blockchain’s potential applications and benefits in addressing future urban challenges is an emerging field of research which has not been fully investigated. In fact, what makes blockchain attractive for smart cities is the design scheme and underlying protocols.

Accurately identifying and analyzing vulnerable roadway users (VRUs) such as pedestrians, bicyclists, and other non-vehicle occupants, are a crucial yet difficult undertaking. VRUs’ behavior is influenced by localized factors such as land use, and their movements are not confined to predefined paths. This study will investigate the use of emerging technologies such as LiDAR, network cameras, and AI/ML algorithms to capture the movements and behaviors of vulnerable road users (VRUs).

This project stands at the intersection of cognitive psychology, AI and computer vision, and transportation safety and efficiency.

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.

Technology has changed individuals’ travel behavior and time-use in so many ways. As much as it offers variety of benefits to societies, it may add to social exclusion phenomena, since the need for travel is being replaced by a click of a button in cell-phone. People don’t feel the need to leave their home to carry out their tasks. They work from home, they order their items online, and even if they want to attend a meeting, they no longer are obliged to travel.

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.