The aim of this project is to collect data on the recovery processes due to natural disasters to develop time and cost estimation models for post-disaster recovery activities, identify tipping points to timely post-disaster recovery processes, and determine effective policies and educational programs which prevent substantial delays in the restoration period. Due to increasing frequency and severity of natural hazards occurrence, most recovery activities take longer duration than the initial anticipated plan created immediately after the disaster happen.
The United States has witnessed several natural disasters in recent memory. Natural disasters such as hurricanes, wildfires, and floods not only cause extensive monetary damages but also lead to spatio-temporal displacement of affected residents. Often, in these scenarios, governments at various levels – state/regional/local – grapple with how to effectively evacuate those affected while ensuring their safe relocation, and minimal risk.
It is predicted that half of the vehicles sold and 40% of vehicle travel could be autonomous in the 2040s. However, how the presence of connected and autonomous vehicles (CAV) impact highway capacity and network system performance remain unclear. Without this knowledge, it is hard to understand and quantify the implication of the disruptive CAV technologies on the existing traffic operations. Also, it would be difficult for relevant agencies (e.g., MPOs and state DOTs) to make appropriate long-term planning for preparing the infrastructure systems for emerging mixed CAV traffic.
This project will develop and validate a multi-criteria decision-making approach for enhancing the longevity of transportation infrastructure built on problematic test site conditions that includes poor subsoil conditions. In the United States, the annual cost of damage to constructed facilities built on problematic subsoil conditions was approximately $13 billion, and a significant portion of this amount can be attributed to damages sustained by pavement infrastructure. With continuing pressure on transportation agencies across the nation, several techniques including replacing the existing material and treating the problematic soils were implemented.
Autonomous vehicles present a unique opportunity to increase safety, roadway capacity, and even extend services to people with disabilities. At the same time, their potential impact with respect to decreasing transit ridership of established systems has not yet been well studied, along with the corresponding sustainability implications. Increased transit ridership decreases the number of vehicles on roadways and decreases the environmental impact of passenger transportation. However, It is often less convenient compared to a personal automobile with respect to convenience.
Assessing the impacts of new and disruptive technologies on automobile usage and the modal split is emerging as a key issue for transportation planners and policymakers. The proposed research will offer a new approach to quantifying the impact of TNCs (Transportation network companies such as Uber and Lyft) on VMT (Vehicle‐Miles Traveled). The approach is based on a simple idea from counterfactual theory, which is to compare VMT estimates after the TNCs introduction to a region to what the VMT would have been without the TNCs.
A road network, consisting of different, but interdependent, transportation infrastructure assets, such as pavements, bridges, signs, etc., supports the mobility, economy, and safety of our society as a whole. According to the 2017 American Society of Civil Engineers infrastructure report card, the U.S. highway system has been underfunded for years. In 2015, 21% of highway pavements are in poor condition, which costs motorists $120.5 billion in extra vehicle repairs and operating costs. Over all, there is a need of $836 billion in repairs and capital investment for America’s highway system.
Setting priority in highway improvement projects where safety consideration plays a differentiating role in the decision making process. As such, quantitative safety is now being recognized as an important element in the project selections process at the planning phase. Quantitative evaluation of safety performance of particular roadway facilities, for example, segments and intersections, is critical to understand where the safety concerns need to be addressed on a priority basis. Moreover, it is also important to implement appropriate safety improvements to prioritized set of locations where promise of safety benefits is potentially high.
The Journal of Public Transportation (JPT) is an international peer-reviewed open access journal published by the University of South Florida through the Center for Urban Transportation Research on a quarterly basis. The Journal is now in its 22nd year and has been published solely online since 2014. It is available free via Scholar Commons (http://scholarcommons.usf.edu/jpt/), and is indexed in Scopus, the Social Sciences Citation Index, SocINDEX, TOC Premier and Urban Studies Abstracts.
The impact of predictive safety assessment based on quantitative methodology of the Highway Safety Manual (HSM) is significant particularly in urban roadway facilities. The responsibilities of safety professionals, transportation planners, and decision makers are critical for safe and efficient transportation in the ever-increasing travel demand in urban areas. The purpose of this study was to develop a quantitative safety assessment tool of converting one urban roadway facility type to another with the application of predictive methodology and principles in the HSM.