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.
A broad API will be developed to handle interfacing any simulation with a multi-agent demand simulator. This will be tested on the existing MATSim-NYC (which will be enhanced to include freight and parcel delivery activities) and aBEAM implementation, BEAM-NYC, for three use cases in electric transit, freight, and traffic.
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 COVID-19 outbreak has dramatically changed travel behavior in cities across the world. With changed travel demand, economic activity, and social-distancing/stay-at-home policies, transportation systems have experienced an unprecedented shift in demand and usage. Since the start of the pandemic, the C2SMART research team has been collecting data and investigating the impact of COVID-19 on mobility and sociability.
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.
In previous years, the research team has developed and calibrated a base model implemented in MATSim and SUMO. This virtual testbed simulates an 8-million-person population and includes cars, trains, bus, bikeshare, taxi, and other for-hire vehicles calibrated to the year 2016. The team is building the architecture to host this virtual test bed and developing system design and user guide documentation.