Connected Cities for Smart Mobility toward Accessible and Resilient Transportation
In this hands-on course, you’ll learn how to use Unity 3D to build 3D models of road networks. You’ll start with the basics of Unity 3D, including its interface, GameObjects, components, and scripting. From there, you’ll dive into building 3D models of road networks, covering elements such as roads, buildings, and terrain.
Dr. Lili Du will discuss the EP fleet management problem, which is mathematically modeled as a vehicle routing problem (i.e., mE2-VRP), aiming to optimally dispatch the minimum number of EPs to approach and serve the EDs using different proportions of EV flows to save EDs’ travel time and mitigate traffic congestion to different extents in different network congestion and charging station coverage scenarios. She will also discuss suggestions for improving the service efficiency of CaaS + .
This course covers the basics of graph neural networks (GNNs), including graph convolutional networks, graph attention networks and graph wavelet networks. The session aims to provide a solid understanding of GNN modeling for machine learning practitioners, data scientists, and anyone interested in AI advancements.
In this course, you will learn how to harness the power of OSMnx, a Python library for extracting and visualizing Open Street Maps data. Through hands-on exercises, you will gain practical experience in using OSMnx to model and simulate projects and have a solid understanding of this Python library and be able to apply OSMnx to real-world problems.
In transportation planning, volume-delay functions (VDFs) are essential functions used for traffic assignment and network design problems. However, the static Bureau of Public Roads (BPR) function, created by the
C2SMART is thrilled to welcome members of faculty from KAIST (Korea Institute for Advanced Study of Science and Technology).
COVID has exacerbated two emerging trends in transportation analysis: (1) the rise of passively-generated big data; and (2) the increasing need to deal with the “unexpected” disruptions. This talk emphasizes the need for learning big and small data for transportation planning and resilience analysis. Different ways of learning are described, with applications ranging from long-term planning analysis to rapid responses under disruptions.
This presentation introduces a deep choice framework that synergizes DNNs and DCMs to model individual travel decision.
The webinar will discuss the U.S. DOT Intersection Safety Challenge Prize Competition, including a program overview, the Prize Competition structure, and Stage 1A expectations.
In this talk, Professor Fosgerau presents results from application to large datasets (1,337,096 GPS traces of car trips, 280,000 GPS traces of bicycle trips) in Copenhagen.
Presented by: Joseph Chow, Associate Professor, NYU Haggai Davis, PHD Candidate, NYU Tuesday, June 13, 2023: 12:00pm - 1:00pm ET | Virtual
Presented by the Transportation Research Board (TRB) subcommittee AEP30(2) Route Choice and Spatio-Temporal Behavior Speaker: Professor Marcela A. Munizaga, Universidad de Chile.