Connected Cities with Smart Transportation
This talk will present tractable models of road traffic and transit which feature positive feedback, whereby my travel choice (e.g., whether to travel, what mode to take, etc.) leads others to make
This engaging event will take place on December 11th at 5pm, concluding with a cocktail reception, where you can connect with industry leaders and peers. During the conference, we’ll explore
Digital Twins as a Highly Responsive Decision Support tool for Management and Planning of Transportation Systems Operating Under Stochastic Demand and Supply Conditions This workshop will focus on the development
The main theme of the 8th NYUAD Transportation Symposium is Transportation and AI: Opportunities and Challenges. Artificial Intelligence (AI) has the potential to revolutionize the transportation industry, by increasing efficiency
The MobilityCoin is a new currency for paying for all your daily trips within a metropolitan area. At the beginning of each year, the MobilityCoin Agency allocates a specific number
Presented by Qian Xie, Cornell Hyperparameter optimization is crucial in real-world applications such as machine learning model training, robotics control, material design, and plasma physics. In transportation, hyperparameter optimization plays
The ubiquity of GPS-equipped mobile devices has enabled the collection of human mobility data with high spatiotemporal granularity. Indeed, there now exists an ecosystem of both data providers and consulting
This year's panelists are Dr. Semiha Ergan, Professor at NYU; Geline Canayon, Senior Project Manager at Aimsun & Vice President of ITE MET Section; and Alia Kasem, Senior Data Scientist at NYC
In this seminar, I will introduce GPUDrive, a high-performance, data-driven driving simulator that operates at 1 million FPS. GPUDrive is built on the Madrona Game Engine and uses GPU acceleration
The increasing frequency and severity of drought events worldwide demand innovative approaches that can effectively characterize, track, predict, and assess impacts of the complex spatiotemporal dynamics of water scarcity. This
Abstract: In this course, we will discuss the decision theory of random utility maximization and discrete choice models (DCMs) including multinomial logit (MNL), nested logit (NL), mixed logit (MXL), and
Autonomous vehicles (AVs) rely on deep neural networks (DNNs) for critical tasks such as environment perception—identifying traffic signs, pedestrians, and lane markings—and executing control decisions like braking, acceleration, and lane