How can we create ����������������community food security���������������? This project aims to develop a community-based socially intelligent nonprofit food rescue and distribution infrastructure to fairly serve vulnerable communities experiencing food insecurity. This infrastructure will have a loop mechanism that continuously learns consumer preferences and provides feedback to upstream stages of the supply chain and also learns about the food availability at the local food sources and feeds that information to the downstream stages. The main objective of this research is to minimize food waste along different stages of the supply chain while maximizing equitable access to safe food given consumer preferences. Food banks are nonprofit organizations that provide a framework for the non-profit food supply chain by collecting donations from multiple sources such as local grocers, growers, and the community (e.g., food drives) and distributing the donations to food-insecure households through a network of community-based partner agencies (e.g., food pantries, homeless shelters, schools). The COVID-19 pandemic has significantly strained this network as demand has surged, the volunteer-based workforce has waned, and supply uncertainty has increased highlighting both the network������������������s strengths and limitations and the need to strengthen the community-based infrastructure and create solutions that are self-reliant and robust for communities that are affected by such events.

Electric vehicle (EV) technology encourages sustainability benefits by reducing environmental pollution primarily associated with transportation-related activities. With the EV market share anticipated to exceed half of all new vehicles sold by 2040, there is an urgency to begin preparing for an EV future now. Therefore, there is a need to develop a statewide EV network expansion plan that requires establishing policies, planning practices, technical siting guidance, and electric power grid requirements designed to accommodate an extensive EV charging load. On the other hand, to ensure EV charging access is accessible throughout the state, including historically underserved communities such as rural areas and communities of concern, equity best practices must be established and incorporated into the plans. A failure to create a viable and reliable EV infrastructure and associated policies will hinder EV adoption and exacerbate the health and environmental impacts caused by the transportation sector. The proposed Project will develop a series of planning and policy best practices and technical guidance for siting EV charging infrastructure to support the expansion of the charging network and its management in North Carolina. This research will assess local planning policies and power utility considerations to develop guidance that informs the efficient and equitable development of a statewide EV charging network plan. The policy and planning research tasks will result in a practice-ready guidance document. This document will include guidance for local agencies, draft policies that can be locally adopted to simplify EV infrastructure permitting and approvals at the municipal and county level, and guidance that highlights opportunities for NCDOT to collaborate and support external partners in improving statewide EV infrastructure. Additionally, the technical guidance derived from models for siting EV charging infrastructure will support the charging network���s expansion and provide insights on charger deployments given geographical limitations, travel demand constraints, electric power grid requirements, and equity considerations, among other concerns. The research results will be practice-ready, implementable guidance on EV network siting and development. The policy and planning best practice and EV development guidance can be used to support MPO, RPO and local planning agencies for siting local EV infrastructure and developing planning policy that encourages the establishment of an equitable and technically sound EV charging infrastructure.

The overarching goal of the proposed research is to identify, test, and evaluate technologically enabled and community-supported solutions for temporally distributing travel demand for on-demand public transportation services in an equitable manner, without the use of traditional pricing incentives. We are specifically interested in understanding whether enabling and incentivizing prosocial behavior, such as volunteering to shift one������������������s trip time to accommodate others, share a ride, and cooperate with other users to improve outcomes for the user community or to prioritize a transportation disadvantaged user, is a potential solution that is feasible and desirable for communities. If our preliminary analysis during the proposed planning grant (PG) supports the case of cooperative adaptive ride planning, we will investigate how prosocial behavior can be enabled in a trip scheduling environment and be facilitated through the use of artificial intelligence (AI), and we will test and evaluate the efficacy of this approach in improving service during an integrative research grant (IRG). No previous research has explored empathy and prosocial behavior in the context of traveler choices and decision-making.

The objective of this RAPID project is to identify and document possible issues of the existing COVID-19 vaccine distribution and administration systems and propose solutions through collecting national vaccine distribution and administration data and quantifying lead times and various performance measures. We plan to collect day-to-day vaccine allocation and shipment data (to track the supply over time) and vaccine administration data from CDC and States. More information on data elements is available in the research plan section. While this data is available at CDC now, it is not available to the public and it is not archived for a long time (as is the case for N1H1 vaccination data) and will be lost if not collected now. We will work with reporters that have collaborated with us before to create a freedom of information act (FOIA) request to access and archive the data. State record this data as well; however, they do not follow a consistent way of reporting the data, and the majority of them only report cumulative data. It is not certain whether they record the daily data, and if so, how long they keep it. As such, the daily data is at great risk of being lost if not collected as soon as possible and many important vaccination data and trends will be lost if the day-to-day data is not available.

State highway agencies own and maintain a substantial number of operations equipment assets, which are diverse in type and condition. The operations equipment are critical components for delivery of State highway agency programs, projects, and services and contribute to a significant portion of capital investments. Operations equipment assets generally deteriorate as they age resulting in rising operations and maintenance costs and decreasing salvage values. The deterioration conditions and technological changes motivate agencies to replace a portion of their equipment fleet periodically. Operations equipment assets require recurring maintenance, operational expenditures, as well as timely replacement to retain their value and achieve their anticipated level of performance, reliability, and economy. According to practical considerations also highlighted in the NCHRP Project 13-04 (Hamilton, 2018), budget cuts often hit the replacement funds, as a fast solution to expenditure reduction. However, in the long run, keeping, operating, and maintaining an aged fleet may cost highway fleet agencies and public more than a younger fleet. For instance, maintenance and repair costs of an aged equipment increase, as does its downtime, both of which directly affect the service delivery and task accomplishment capacities. Besides, the service level of old equipment is often limited: its increased downtime hours lead to under-performance and/or inefficient performance of essential services. Therefore, rather than short-term solutions that may not efficiently save resources, fleet management forces shall be equipped with adequate tools and effective strategies for making long-range (i.e., 20-25 years) plans and budgets for replacement of highway operations equipment. The existing replacement decision frameworks are usually based on a desire to minimize the total or expected life-cycle fleet costs, including those related to the acquisition, operations and maintenance, and salvage value. Yet, life-cycle costs may not capture the fleet management needs and budgets over a long-range horizon. Therefore, current practices, although representing scientific rigor and practical aspects of fleet management strategies, may lack the background required for long-range fleet management. In fact, there is no widely accepted process for determining the long-range plans and budgets for replacement of highway operations equipment. Therefore, there is a need to develop a practically feasible and theoretically sound methodology for long-range equipment replacement planning that accommodates the needs of highway agencies and their budget constraints. Literature presents various solutions to making investment decisions for highway operation equipment (e.g., Hamilton 2018; Fan et al. 2015). A variety of practices and strategies have been used by State departments of transportation (DOTs) and highway agencies to make investment decisions for highway operation equipment, while taking the diverse classifications of such assets into careful consideration. For instance, as of 2002, Florida has used thresholds for mileage or age to determine vehicle replacement priorities. For example, full-size pickups are replaced at 8 years or 95,000 miles and dump trucks at 10 years or either 150,000 or 250,000 miles depending on capacity (Weissmann & Weissmann 2002). Besides, Kim et al. (2009) conducted a study to improve the Oregon DOT������������������s existing fleet replacement model. The study confirmed that most DOTs used fixed thresholds as a primary factor in equipment replacement decisions. These fixed thresholds for replacement decisions do not yield the optimal fleet management criteria, especially over a long horizon. Texas Department of Transportation (TxDOT) has used the Texas Equipment Replacement Model (TERM) to identify equipment pieces as candidates for replacement one year in advance. TxDOT������������������s equipment operations system (EOS) captures extensive information on all aspects of equipment operations. EOS considers three criteria for replacement: 1) equipment age, 2)