This project addresses the contemporary challenges faced by the transportation workforce, influenced by demographic shifts, labor market fluctuations, and the growing demand for interdisciplinary skills. Using a case study of the southeastern United States, five main objectives guided the project: a) synthesizing the current state of workforce development practices, b) identifying key challenges in the transportation workforce, c) defining the term "workforce development" within this context, d) exploring potential roles of University Transportation Centers (UTCs) in tackling these challenges, and e) offering actionable recommendations for enhancing transportation workforce development. The research used findings from a literature review, stakeholder meetings, a survey of transportation professionals, and personal interviews with selected experts. The findings were integrated to derive conclusive results instead of independently interpreting each dataset. The study revealed that workforce development hinges on stakeholders, recruitment strategies, educational aspects, and diversity initiatives. The most pressing challenges involved changing labor market trends, demographic shifts, and the necessity for interdisciplinary skills. Workforce development was conceptualized as strategic measures for recruiting, retaining, educating, and training the present and future transportation labor force to meet identified challenges and needs. The UTCs' potential contributions were identified in facilitating recruitment, inspiring interest in transportation careers, and creating resources for continuous education and training. Key recommendations involve interdisciplinary educational initiatives, specialized training, and resource development to assess and enhance existing training strategies.

Previous studies mainly substantiate the influence of neighborhood design on older adults’ travel behavior. This study goes beyond and examines how smart growth affects older adults’ travel behavior over time in the central Puget Sound. Using regression models for the years 1999, 2006, and 2014, we find that smart growth factors have significant but changing effects. The regional growth centers play a growing role in reducing older adults’ travel distance, trip frequency, and promoting non-car commuting modes. This study adds to the knowledge of how older adults’ travel behavior can be affected by the multilevel and long-term urban development strategies.

Safe Routes to School (SRTS) programs, initiated by the U.S. Department of Transportation, aim to promote active modes of transportation (walking and cycling) among students commuting to school through several means, including infrastructure improvements and educational programs. A review of SRTS programs at the state level reveals that there is no standard framework to quantify and prioritize the needs of school districts or communities. The primary objective of the study is to develop a systematic and data-driven framework to identify site-specific infrastructure improvements that have the potential to positively affect student safety and mobility. There is limited literature on risk factors associated with bike and pedestrian crashes around schools. This study investigates roadway infrastructure and socioeconomic, demographic, and land use characteristics to identify risk factors affecting the safety of bicyclists and pedestrians around schools. The study encompasses an analysis of around 3,000 schools in the State of Florida and tests over 20 potential independent variables to develop safety performance functions assessing the safety of bicyclists and pedestrians near schools. The research reveals significant factors influencing the risk of school-related bike and pedestrian crashes, including school location, the number of schools in the service area, intersections with stop signs, retail land uses, the median age of the population in the service area, median household income, and the proportion of the white population. Practitioners can adopt the models to prioritize schools for SRTS infrastructure investments.

Global warming is expected to increase 1.5 °C between 2030 and 2052. This may lead to an increase in building energy consumption. With the changing climate, university campuses need to prepare to mitigate risks with building energy forecasting models. Although many scholars have developed buildings energy models (BEMs), only a few have focused on the interpretation of the meaning of BEM, including climate change and its impacts. Additionally, despite several review papers on BEMs, there is no comprehensive guideline indicating which variables are appropriate to use to explain building energy consumption. This study developed building energy prediction models by using statistical analysis: multivariate regression models, multiple linear regression (MLR) models, and relative importance analysis. The outputs are electricity (ELC) and steam (STM) consumption. The independent variables used as inputs are building characteristics, temporal variables, and meteorological variables. Results showed that categorizing the campus buildings by building type is critical, and the equipment power density is the most important factor for ELC consumption, while the heating degree is the most critical factor for STM consumption. The laboratory building type is the most STM-consumed building type, so it needs to be monitored closely. The prediction models give an insight into which building factors remain essential and applicable to campus building policy and campus action plans. Increasing STM is to raise awareness of the severity of climate change through future weather scenarios.

Travel has become less common due to COVID-19. While prior research has discussed recent travel changes for Americans in multiple ways, few have examined the adjusted travel that has been sustained since March 2021. In addition, little is known about changes in Americans' travel patterns in trips by distance. In this research, we asked two questions: 1) How have the numbers of trips by distance changed since 2019? and, 2) What are the geospatial patterns of the changes? Data from mid-March to mid-September 2021 indicates a 7% decrease in the number of trips and a 14.5% increase in people staying home. People traveled less except for those in the middle U.S. states, from North Dakota to Texas, as vertically aligned. Staying home more seemed to occur mainly in the South. Trips between 50 and 500 miles increased nationwide. COVID-19 has had different levels of impact on trips of different distance ranges.

While several studies have begun to quantify the problems presented by curb conflicts surrounding delivery vehicles, especially unauthorized parking behavior, fewer have specifically examined these issues through the lens of driver behavior. To gain on-the-ground insight into competition for curb space, what issues drivers face, and the circumstances that inform their parking choices and related behaviors, we use data from Reddit to examine US driver perceptions and behavior, posing the following questions: What are some key challenges parcel delivery drivers encounter when delivering in urban areas? What strategies do drivers employ when parking their vehicles to make deliveries? What reasons do drivers cite for engaging in unauthorized or questionable parking practices? We find that parking is among the largest challenges drivers face while delivering in urban areas, largely because parking difficulties extend the time required to complete many routes. Drivers in our sample preferred to park in authorized spaces, but generally accepted the practice of unauthorized parking to complete their routes. Often, they did so due to lack of available parking, but also for safety and/or expedience. Moreover, drivers reported that parking enforcement personnel rarely issued tickets or other reprimands, acknowledging that for delivery vehicles, unauthorized parking is often necessary. Finally, drivers also described concerns surrounding interactions with other road users while making deliveries, especially in terms of conflict and safety. Curb management policies and freight providers’ practices alike will need to adapt in the face of the changing landscape of the curb.

This dataset contains a list of Reddit posts and the corresponding threads for those posts resulting from targeted searches of four delivery-related subreddits conducted in Fall 2021. We used this dataset to understand driver practices in and views on delivering in urban areas and the challenges they face. It was downloaded using Reddit's API through the RedditExtractoR package for the R programming language. Re-use of this data is subject to Reddit API terms.

Transportation network companies (TNCs), such as Uber and Lyft, offer a new mobility option to consumers. An increasing number of transit agencies work with TNCs, and different types of partnerships have formed. While these service models may serve the general population well, their implications for transportation-disadvantage populations, including older adults, individuals with disabilities, and low-income people, have not received enough attention. These populations are highly dependent on public transit services. Additionally, we have limited firsthand knowledge of challenges that hinder transit agencies and related human service agencies from building partnerships with TNCs. Can these agency/TNC partnerships accommodate the needs of transportation-disadvantage populations? This study explores these issues through a literature review and interviews with 16 related organizations in the State of Florida, where transportation-disadvantage populations are served through a coordinated system but the partnerships with TNCs are still limited. The paper first categorizes the existing agency/TNC partnership service models into three types and examines their benefits and problems in serving transportation-disadvantage populations. It then identifies different organizations’ perceptions of TNCs and the challenges for some agencies to work with TNCs. The general challenges include difficulty in estimating service demand, data sharing problems, hidden costs and staff efforts, training and safety issues, and the need of complementary vendors. The challenges specifically in rural areas are a lack of motivation and commitment among TNCs, affordability issues, and TNCs’ adaptation to the rural geography. These challenges in agency/TNC partnerships need to be addressed to serve the public better, including transportation-disadvantage populations.

With nationwide declines in public transportation ridership, transit may be falling behind in its ability to help cities deal with congestion. Increasing real-estate values are causing the economic displacement of low-income populations, those most closely associated with transit ridership. A plethora of new mobility options are providing alternatives for transit riders who can afford them and even for those who require subsidy. But how will access to transit, ridership, and congestion be impacted by these shifts in demographics and the introduction of new mobility services? In thrust 1, the team assessed the impacts of low-income individuals and families moving to the periphery of communities, ie, the suburbanization of poverty, on public transit. In addition, this thrust provided a detailed analysis of sociodemographic and accessibility changes over time. In thrust 2, the study team developed a novel approach to understand how levels of transit service and demographics impact transit ridership on a highly specific spatial and temporal scale. In thrust 3, the study team developed a better understanding of the interactions between public transit and transportation network company (TNC) providers. In thrust 4, the study team documented the rapid evolution of paratransit services available to access healthcare. Although the research in all four thrusts focused on specific areas of the southeast US, the results are applicable nationally to aid transit and regional planning agencies.

Promoting walking and biking to school can make economic sense for school districts and communities. In the United States, unsafe walking conditions force parents to drive children or school districts to bus students to school and may lead to elevated injury and fatality rates for child pedestrians and cyclists. The costs of these impacts are significant. However, infrastructure investments that make active travel safe create options for children—and all community members. Such investments also reduce school transport and injury costs. This chapter provides data from the United States on the scale of this issue and demonstrates the benefits of investments in non-motorized infrastructure.

Extreme commuters, who spend 90 min or more on one-way commuting, has been a rapidly growing group. A few studies explain extreme commuting from the socioeconomic and land-use perspectives. However, little research takes spatial variations into account, nor does it make a comparison of different transportation modes. This paper applies Geographically Weighted Regression (GWR) model and binary logistic model to explore extreme commuting and its relationship to land-use and socioeconomic attributes in the central Puget Sound. Data used include the Census Transportation Planning Package (CTPP) at the census tract level and the Puget Sound Travel Survey of the individual households. The results demonstrate both significant local variations of the effects of influencing factors and the differences in different transportation modes. These findings indicate that extreme commuting is not necessarily a constrained choice behavior due to socioeconomic disadvantages and that urban land-use planning is significant in explaining extreme commuting.

In developed countries, buildings are involved in almost 50% of total energy use and 30% of global annual greenhouse gas emissions. The operational energy needs of buildings are highly dependent on various building physical, operational, and functional characteristics, as well as meteorological and temporal properties. Besides physics-based energy modeling of buildings, Artificial Intelligence (AI) has the capability to provide faster and higher accuracy estimates, given buildings’ historic energy consumption data. Looking beyond individual building levels, forecasting building energy performance can help city and community managers have a better understanding of their future energy needs, and to plan for satisfying them more efficiently. Focusing at an urban scale, this research develops a campus energy use prediction tool for predicting the effects of long-term climate change on the energy performance of buildings using AI techniques. The tool comprises four steps: Data Collection, AI Development, Model Validation, and Model Implementation, and can predict the energy use of campus buildings with 90% accuracy. We have relied on energy use data of buildings situated in the University of Florida, Gainesville, Florida (FL). To study the impact of climate change, we have used climate properties of three future weather files of Gainesville, FL, developed by the North American Regional Climate Change Assessment Program (NARCCAP), represented based on their impact: median (year 2063), hottest (2057), and coldest (2041).

Emergent technologies like autonomous/connected vehicles and shared mobility platforms are anticipated to significantly affect various aspects of the transportation network such as safety, mobility, accessibility, environmental effects, and economics. Transit agencies play a critical role in this network by providing mobility to populations unable to drive or afford personal vehicles, and in some localities carry passengers more efficiently than other modes. As transit agencies plan for the future, uncertainty remains with how to best leverage new technologies. A survey completed by 50 transit agencies across the United States revealed similar yet different perceptions and preparations regarding transportation network companies (TNCs) and autonomous transit (AT) systems. Transit agencies believe TNC market share will grow, either minimally or rapidly (72%), within the next 5 years and have either a negative (43%) or positive (35%) impact on their transit system. Only 30% of agency boards instructed the agency to work with TNCs, despite no perceived transit union support. For AT systems, 22% of agencies are studying them, 64% believe the impacts of AT over the next 10–20 years will be positive, but fewer agencies are influenced to consider new technologies because of AT systems (38%) compared with TNCs (72%). Surprisingly, transit administration is mostly unsure about driver and transit unions’ perceptions of these technologies. In addition, a significant number of transit agencies do not believe they should play a role in ensuring TNCs are safe and equitable and that TNCs should not have to adhere to the same regulations (50%, 28% respectively).

Over the next three decades, housing and community planners will need to address the challenges presented by the growing numbers of seniors in their communities, and the associated spiraling economic and health care costs. A significant number of seniors may not be willing or able to age in place (i.e., live in the same home and neighborhood of their middle adult years), nor pursue new living arrangements in retirement communities or other traditional senior living models. Are there viable alternatives that can be embedded, integrated, or re-created in the existing residential infrastructure, particularly in the suburbs where the challenges to aging are significant? This chapter envisions a middle ground of residential options between the age-restricted senior living community and the suburban home one has lived in for a long time. An "aging-in-community" paradigm encompasses housing models that allow people to live in age-integrated communities but not necessarily in their current home, or to live in their existing home but with a layer of community-generated service networks to support them in maintaining their homes and lives. These housing models are appearing in urban centers and suburban neighborhoods with access to friends, families, and services. The housing models include naturally occurring retirement communities (NORCs), the Village Movement, accessory dwelling units (ADUs), and multigenerational and senior cohousing where an intentional community of private homes is clustered around shared spaces such as a common house, recreational area, and community garden plots. In each case, these exemplars of "aging in community" draw on reserves of social capital to foster the well-being of older residents who choose not to live in conventional retirement communities or are unable to remain in their current homes without assistance. These new models may have their own particular constraints for certain individuals, but overall, as a viable living option, they offer older adults opportunities for control and connectivity with community while addressing the challenges of affordability.