Accessibility to food stores offering nutritious and affordable options is critical to ensure urban residents lead healthy lives. However, the uneven distribution of diverse food options in U.S. cities has raised concerns about marginalized communities’ ability to obtain affordable and nutritious food. Transportation-related issues, like car-oriented urban form and sprawl, have created food deserts in cities, making it hard for marginalized communities to access affordable and healthy food. This lack of access has recently drawn the attention of urban planners to address food access-related equity issues. Spatial interaction models can help measure food access by examining how grocery store supply, demand, and proximity affect individuals’ access to necessary food. However, these measures do not always consider the pricing of food items, which can significantly affect food access for low-income individuals and families. We use a highly disaggregated measure tailored to measuring inequitable food accessibility by factoring grocery store pricing into various types of supply and demand. Then, using regional socio-demographic thresholds, we identify target population groups and perform a series of equity analyses using our food accessibility measure. Our results show that walking is more inequitable than driving to food stores and that African American and Hispanic minorities, the elderly, and carless households are more burdened than the reference groups (whites). Using our framework, we identify food deserts and provide spatial and empirical insights for policymakers and planners to address food insecurity and promote equitable access to healthy food.

Transportation equity is a vital but vague issue for planners and engineers in the United States, where mobility-based transportation planning has triggered the disproportionate distribution of benefits and burdens in the transportation system. Previously, transportation equity studies have evaluated equity using different transportation service metrics and socio-demographic factors. Job accessibility is one principal metric for transportation quality as it directly relates to a community’s economic prosperity. While most equity studies analyze aggregate accessibility for a single mode, our study measures blue-collar and white-collar job accessibilities for automobile and transit by incorporating job proximity, competition, and matching, using the Dallas-Fort Worth area as a case study. Using Lorenz curve, Gini coefficient, and Atkinson measure, we performed horizontal and vertical equity (within and between groups) studies and compared various population groups by disaggregating the population based on the two most cited environmental justice indicators, namely, income and race. Our findings suggest that (a) job accessibility by transit mode has higher inequalities compared with automobile, (b) white-collar and blue-collar jobs have similar inequality levels, (c) low- and high-income populations experience somewhat the same inequality levels, and (d) inequality is highest among African American communities, (e) blue-collar job accessibility has higher inequality for Hispanic and Asian communities for both transportation modes and (f) the disparities in job accessibility are greater between racial groups than income groups in the Dallas-Fort Worth area. To address such disparities, planners must enhance transit availability through subsidizing transit costs and transit feeder services like on-demand shared services.

Additive manufacturing (AM) or 3D printing is transforming the construction industry by enabling the production of complex structures and components from digital blueprints using materials like concrete, plastics, and recycled materials. This technology reduces material waste, lowers production costs, and opens new possibilities for sustainable and affordable housing. Traditionally used for prototypes and low-volume production, AM has advanced into the architecture, engineering, and construction (AEC) sectors, offering potential solutions to the affordable housing crisis. Concrete 3D printing, for example, can reduce carbon emissions through the use of alternative materials, minimizing the need for raw resources. Additionally, the ability to optimize material usage and reduce construction waste through techniques like prefabrication and rapid construction can significantly lower the cost of building homes. This paper discusses how AM can contribute to addressing the challenges of affordable housing by exploring its applications in construction, its potential for reducing environmental impacts, and its role in improving cost-effectiveness. By integrating AM into manufactured housing models, it becomes possible to develop sustainable, cost-effective homes on a larger scale, which offers a promising solution to the growing demand for affordable housing. Through the widespread adoption of 3D printing technologies, it is feasible to address both affordability and sustainability concerns in the housing sector.

Cities serve as hubs for various activities that necessitate comprehensive transportation connectivity. This study examines the decadal urban agglomeration patterns from 2001 to 2020 and critically assesses the relationship between freeway developments, industrial relocation, and population density in the DFW (Dallas Fort Worth) metropolitan area. Landsat satellite imageries, US census, and open-source GIS datasets have been utilized in the study. The Maximum Likelihood Classification (MLC) algorithm helped generate the vector database, using which Land Use/Land Cover (LULC) variations were assessed. The calculated overall accuracies of the classified images for 2001, 2011, and 2020 were 93.12%, 91.87%, and 93.12%, respectively. Eventually, buffer generation techniques and summary statistics helped detect potential boom hotspots. Our results indicate that the highway advancement project lures industries, leading to population migration. The LULC variations suggest that the increase in highway infrastructure resulted in a surge in built-up and a decrease in open spaces in District-3 of DFW. From our study, we find that 79.16% of old industries are located near old freeways, while 78.84% of new industries are located near new freeways. Further, our industrial area to road area comparison clearly shows that industrial relocation was driven by transportation advancements over time. Our results also confirm that this relocation of industries fostered a massive population influx during the following decades.

Off-system bridges are critical components of the United States’ transportation infrastructure, providing essential access to rural communities and enabling residents to reach vital services such as employment, education, and healthcare. Many of these bridges are structurally deficient, functionally obsolete, and unmaintained. This disproportionately hinders the mobility of underserved populations, worsening socioeconomic disparities. Despite existing research, there is insufficient focus on the unique challenges posed by off-system bridges, including handling the class imbalanced nature of the bridge condition rating dataset. This paper predicts bridge deck conditions by using Generative Adversarial Networks with Focal Loss (GAN-FL) to generate synthetic data which enhances precision–recall balance in imbalanced datasets. Results show that integrating GAN-FL with random forest (RF) classifiers significantly enhances the performance of minority classes, improving their precision, recall, and F1 scores. The study finds that augmenting training data using GAN-FL greatly enhances minority class prediction, thereby assisting in accurate bridge condition modeling.