The project will build a framework to optimize and prioritize locations for FloodNet sensor deployment, for measurement of hyper – local flooding in New York City (NYC).
Overview One of the enduring challenges in statewide transportation planning is that consistent population travel data remains scarce, particularly for underserved and rural communities. Planning models are often only estimated
The Pink Tax is a form of gender-based price discrimination concerning the upcharge women pay for specific products or services. This white paper is based on the conviction that innovations to increase personal safety and improve accessibility for caregivers will provide greater access to education and jobs, deliver health benefits from more active transportation, and support women’s confidence and well-being in trip planning—while greatly reducing carbon emissions.
As part of USDOT’s Connected Vehicle Project, C2SMART researchers at New York University – in collaboration with NYCDOT and industry partners JHK and Harman – recruited volunteer participants with vision disabilities via local and national organizations to help conduct field tests of a phone application, PED-SIG, which could improve mobility of pedestrians with vision disabilities to navigate safely and independently through New York City.
The Equitable Commute Project (ECP), on which C2SMART is a core partner, has been selected as one of six finalists in NYSERDA’s Electric Mobility Challenge. As a finalist, the ECP is eligible to win one of three $7 million awards to help low-income essential workers in transit deserts commute and travel throughout the city.
Over the past three years, researchers at UTEP and NYU have collaborated on the development of a smartphone application, Urban Connector, which is designed to cater to the urban mobility needs and preferences of seniors in El Paso. A prototype of the application was developed and a follow-up survey was conducted to gather feedback. The app was improved to its beta version, and was tested by seniors in El Paso in their day-to-day travels.
Autonomous mobility must be evaluated under more ambitious and holistic standards. This project aims to develop a Responsible Autonomous Mobility framework.
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.
The main deliverable for this project was a smartphone navigation app that addresses the specific mobility needs and priorities of seniors, improving their ability to travel around their cities. After the prototype was developed, the researchers recruited seniors to test the app for a few weeks, and then gathered their feedback.