Transit Accessibility Showcase
Project Description Research Outcomes/Impacts Personnel Sarah M. Kaufman DIRECTOR, NYU RUDIN CENTER FOR TRANSPORTATION Sarah Kaufman is the Principal Investigator for this project Deliverables Datasets Details
Category: Projects
Peruse all current, past, and planned research projects from C2SMART center researchers and affiliates.
Development of Guidelines for Implementing the Flipped Left Diamond Interchange Design
Project Description Research Outcomes/Impacts Personnel Yi Qi Associate Professor and Chair, TSU Yi Qi is the Principal Investigator on this project. Mehdi Azimi Assistant Professor, TSU Mehdi Azimi is a
Reducing US Transit Costs: An Empirical Review and Comparative Case Study of Portland, Manchester Rail Systems
The cost to build and operate transportation infrastructure, including mass transit, in the United States is consistently higher than it is elsewhere in the developed world. As America’s population becomes increasing urban, addressing this issue will become increasingly important. This study seeks to understand why this cost discrepancy exists, and what to do about it, through a review of existing cost data (using operations costs from the US and International governments, and capital cost data from prior studies) and a comparative case study analysis. Two light rail systems, MAX (in Portland, Oregon) and Metrolink (in Manchester, UK), share many design and operations characteristics, and recently completed two similar capital projects. While MAX’s operations and capital costs are lower than the national average, they remain above comparable costs for Metrolink. This similarity in specifications, combined with a divergence in cost, provides an opportunity to understand why US transit is comparatively expensive.
Design Finite Element Analyses and Crash Testing
The objective of this study is to develop a model that links the resource requirements for Capital Program delivery functions with the NYSDOT Capital Program.
Blockchain for Preserving Privacy in V2X Connected Vehicle Applications in Urban Environments
C2SMART researchers developed a more efficient, secure, blockchain-based system to story mobility data on a distribute ledger. To store this data at scale, researchers leverage InterPlanetary File System (OPFS), a scalable distributed peer-to-peer data storage system, and develop efficient consensus algorithms to prevent users from injecting malicious or fake trajectories into the ledger.
Evaluation of Integrated Overweight Enforcement System using High Accuracy WIM System and Non-Proprietary ALPR System
Overview Highway transportation is the most common mode of transport in the U.S. for the movement of goods and services, as it provides quick delivery of shipments traveling short distances
Exploring Cost-effective Computer Vision Solutions for Smart Transportation Systems
This project is focused on developing a deep learning based data acquisition and analytics tool using vision-based sensors (i.e., cameras) to understand cities with machine eyes.
Urban-DEPOT: Deploying EV Charging Infrastructure in an Urban Environment
Overview Transportation is a major source of greenhouse gas emissions and air pollution, with emissions from light-duty vehicles constituting its major share. For example, the light-duty vehicles in New York
A Cost-effective Approach Towards Building a Traffic Sign Data Inventory Using Open Street Images
Traffic signs are critical assets for roadway and infrastructure management. They are also in a great variety and different conditions. According to the asset management plan proposed by US DOT, the research team proposes a cost-effective approach to build a traffic sign data inventory using open street images.
Work Zone Safety: Virtual reality-based traffic co-simulation platform for workforce training and pedestrian behavior analysis
Building off of the research team’s previous work on a smartwatch alarm application and worker attention monitoring system, this project will expand the scope to a) understand workers’ behaviors to modalities of alarms in real physical work environments, and b) improve the VR based traffic co-simulation platform to co-simulate workers position in SUMO in real time as obstacles to be recognized and calibrate the vehicle trajectories in SUMO through larger work zone/traffic vehicle trajectory datasets.