Lateral water drainage on roadways is important to ensure safe and efficient operation and structural condition of the pavement. Pavement rutting could lead to failure in draining water, which poses a hydroplaning risk to drivers due to ponding and loss of skid resistance in wet weather. Traditional data collection methods to identify pavement sections with deformation such as rutting are time-consuming, labor-intensive, and require data collectors to be located on the road, which poses a safety hazard.
Driving cessation in older adults can present a significant transportation problem and public health dilemma. In particular, previously car-dependent older adults may struggle to access healthcare, attend social activities, and conduct errands once they lose the ability to drive. The “Healthy Buddy” project (https://www.hbuddy.org) is a community-based initiative that pairs trained college students with transportation disadvantaged older adults to help them identify existing transportation and health resources in their communities.
Electric bicycles, and electric bicycle sharing programs, represent a new technology in the transportation system. E-bike sharing programs have the potential to replace current modes of transportation, while also improving the mobility of disadvantaged populations. This work will use a survey approach to gather data as to who the users of the e-bike share are and the types of trips which they are making with the e-bikes.
In the U.S. and throughout the world, engineers and planners recognize the need for streets to support safe mobility for different travel modes and types of users. Complete streets policies capture this aim of inclusivity by emphasizing design for older and younger travelers as well as people with disabilities. Issues of gender, families, and caregiving are implicit in this idea of a complete street. On average, women have more household responsibility for accompanying younger, older, and less mobile travelers.
According to the latest Urban Mobility Report published by Texas A&M Transportation Institute (TTI), urban traffic congestion, mostly generated on urban arterials, is a persistently growing problem. In 2017, the total congestion cost in 494 U.S. urban areas was $166 billion and the extra travel time was 8.8 billion hours. Urban congestion is negatively affecting the economy and society of U.S. To solve the urban congestions, the University of Texas Arlington and Georgia Institute of Technology will collaborate to investigate a new Public-Private-Partnership (PPP) Data Sharing Policy through developing a novel arterial system performance monitoring and optimization system.
This project would complement the Transportation Equity Scorecard ─ a tool for project screening and prioritization ─ that is currently being developed by the project team with funding from CTEDD. For this RFP, we propose developing a needs assessment audit tool and supporting guidance to demonstrate how the equity criteria and methods in the Scorecard would be applied by MPOs and local governments to assess the transportation needs of communities of concern, including minority and low-income households, LEP populations, children, elderly, and persons with disabilities. This project continues a prior study, and together the two form a comprehensive approach from needs identification to project prioritization in the form of a Transportation Equity Toolkit.
Over the last decade, globalized supply chains, restructured logistics and freight transportation practices, and exploding online shopping have influenced how goods are produced, transported, stored, and sold. All these changes have resulted in substantial shifts in the spatial distribution of freight activity, as well as vehicles crashes that involved at least one freight vehicle (freight vehicle crash). As a case study, we examine the correlation between development patterns and freight vehicle crashes on city streets in Dallas-Fort Worth (DFW), TX.
The City of Fort Worth, Texas, has requested the University of Texas at Arlington to develop a framework, capacity analysis, and strategic development plan for the Fort Worth Medical Innovation District (FW-MID). We envision this project in two phases. The first phase responds to the intent of the City to conduct a capacity analysis and develop a comprehensive framework about the FW-MID.
This project aims to leverage innovative techniques to develop an intelligent system that assists blind pedestrians to decide when it is safe to cross streets, especially at the uncontrolled crossing locations, where neither traffic lights nor STOP signs are available.
Motivated by the priorities highlighted by Texas Department of Transportation (TXDOT) and following the guidelines in the recent presidential “Executive Order on Maintaining American Leadership in Artificial Intelligence” in 2019, this proposal aims to utilize the state-of-the-art tools and techniques in the field of Artificial Intelligence and Data Science to automatically identify and report traffic-related anomalies and hazards using live traffic camera footage across major highways and arterial roads in the State of Texas.