Autonomous Vehicle Good Citizenry Standard
Autonomous mobility must be evaluated under more ambitious and holistic standards. This project aims to develop a Responsible Autonomous Mobility framework.
Peruse all current, past, and planned research projects from C2SMART center researchers and affiliates.
Autonomous mobility must be evaluated under more ambitious and holistic standards. This project aims to develop a Responsible Autonomous Mobility framework.
The purpose of this study is to develop and implement an analytical framework to calculate deterioration rates for bridges and large culverts based AASHTO-Element inspection data as well as NBI data and demonstrate the application of the approach through currently available inspection data. This analytical approach will be applied to generate deterioration rates for NYS bridges based on, but not limited to climate and/or geographical location, DOT Region, bridge ownership, material types, design types, and bridge types. The outcome of the research will be further implemented in the AASHTO BrM and the Agile Assets Structures Manager and Bridge Analyst.
As C2SMART heads into its sixth year of projects, we are reshaping our RFP process to solicit a project slate which will be bigger and bolder than ever before. We are looking for projects which showcase possibility, addresses complex challenges, broadens collaboration, and directly strengthens the transportation field. This includes research and development projects, student-led initiatives, and non-traditional research in the form of workshops and workforce development.
The research team will first establish a test bed for the development of the advanced WIM (A-WIM) system by collaborating with local transportation agencies for the selection of the test bed site near a static weighing station. Then, it will develop a set of calibration procedures to guarantee that the level of accuracy is reached and preserved over time. These procedures will include, but are not limited to, the effect of temperature, humidity, and pavement type.
This project will use analytical and simulation-based tools for bus network redesign in the presence of ride-hail/for-hire vehicle (FHV) services, particularly for areas regarded as transit deserts.
This project aims to improve the efficiency of mobility-on-demand services with the help of machine learning. The goal is to create an algorithm that public paratransit services, private rideshare companies, and future autonomous vehicle fleets could use to improve operations and lower costs.
This project aims to quantify the various safety, environmental and financial benefits of connected vehicle technologies applied to the New York City municipal fleet.
This project will develop a mobile app that streamlines the reporting of mis-parked dockless scooters and bikes, relaying data to companies responsible and/or local governments while generating a data set that can support a variety of research questions.
This project aims to extend and field-test CAV-based traffic signal/vehicle control methods developed by the research team in previous projects to understand and quantify the benefits of CAV-based control in the real world.
Led by NYU Rudin Center Assistant Director Sarah Kaufman, the Emerging Leaders in Transportation program develops early-career transportation professionals to develop and promote innovations within their organizations. The three-day program includes professional development with executive leaders, communication work through networking activities, and site visits to major transportation management locations.