We present a control barrier function (CBF) approach for headway management in fixed-line transit systems (FTLSs) through vehicular speed control. We frame headway management as a formal property in terms of vehicle time-differences. Using a …
Along with the rapid development of new urban mobility options like ride-sharing over the past decade, on-demand micro-transit services stand out as a middle ground, bridging the gap between fixed-line mass transit and single-request ride-hailing, …
Vehicle bunching is a major problem for transit operators. When vehicles bunch together, the lead vehicle will service the majority of passenger demand, leaving the following vehicles to operate below capacity, wasting fuel and money. Furthermore, …
This project will develop **artificial intelligence techniques to improve the operation of large-scale infrastructure systems**, such as smart transportation networks and electric power grids, which are essential to modern life. These complex systems, known as societal-scale cyber-physical systems, integrate physical infrastructure with thousands of sensors, computing devices, and actuators. Due to their scale and distributed nature, managing these systems in real time poses a significant challenge. To address this challenge, the project will leverage **deep reinforcement learning**, a form of artificial intelligence that learns optimal decision-making strategies directly from data. By improving the efficiency and reliability of critical infrastructure systems, the research will further the national interest through reducing traffic congestion, improving emergency response times, and increasing the stability of the power grid.
In modern transportation networks, adversaries can manipulate routing algorithms using false data injection attacks, such as simulating heavy traffic with multiple devices running crowdsourced navigation applications, to mislead vehicles toward …
Transit agencies that operate on-demand transportation services have to respond to trip requests from passengers in real time, which involves solving dynamic vehicle routing problems with pick-up and drop-off constraints. Based on discussions with …
High-resolution origin–destination (OD) tables are essential for a wide spectrum of intelligent transportation systems applications, from modeling traffic and signal timing optimization to congestion pricing and vehicle routing. However, outside a …
A fundamental problem in multi-task learning (MTL) is identifying groups of tasks that should be learned together. Since training MTL models for all possible combinations of tasks is prohibitively expensive for large task sets, a crucial component of …
Public transportation systems worldwide face persistent challenges of efficiency, reliability, and sustainability. In this chapter, we explore how AI-powered approaches can reimagine transportation planning and operations, with particular emphasis on …
Public bus transit systems provide critical transportation services for large sections of modern communities. On-time performance and maintaining the reliable quality of service is therefore very important. Unfortunately, disruptions caused by …