This project is a continuation of a C2SMART funded project from 2021 titled “Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems.” In the phase 1 project, the team built a digital shadow of campus civil infrastructure and visualized impacts of construction project schedule on the surrounding transportation infrastructure. Phase 2 is focused on expanding the work accomplished in phase 1, to extend the digital twin and enable live data feeds.
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.
Digital Twin (DT) technology represents the next evolution in a gradual shift from physical to digital models in civil engineering. Computer-Aided Drafting (CAD) revolutionized the industry by reducing the time and costs associated with documenting the design. Building Information Modeling (BIM) has since all but eliminated the need for physical design descriptors (i.e., drawings or physical models). A digital twin is a relevant abstraction of the physical asset. Itis most frequently used to model/improve/control manufacturing systems. Civil engineering applications of DT have been starting to emerge, but transportation infrastructure represents a challenging extension of DT technology because of its spatial scale and voluminous and time-varying data. However, DT is a powerful decision support tool for the design, maintenance, and management of transportation infrastructure, particularly for studying the interdependency with other infrastructure systems.
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.
This project aims to develop a student parking lot zoning and zone permit pricing model, and then integrate the total demand model, “base price” model, and the zoning model into a software tool named Sparkman, which can be used by all university parking offices.
This project aims to develop LOS analysis procedures that estimate or measure the average search time for selected types of parking facilities. Additionally, alternative evaluation methodologies for parking operations based on the IOT will be explored. The new smart cities approach to measure customer service is dubbed PMS to distinguish it from the LOS analysis procedure. Cities are interested in making better use of smart meter usage data.
This research project will investigate the design and operations of dedicated lanes for fully automated trucks, the suitability of existing infrastructure to accommodate these novel technologies, and the potential economic ramifications on the surrounding region. The project will use the I-10 Freeway in El Paso, Texas, from the New Mexico border in the west to milepost 55 in the east, as the testbed.