# Equity in Transportation Asset Management: A Proposed Framework
## 2 Materials and Methods
### Analysis  
A comprehensive dataset was used as a case study in this research. The data was publicly available, containing sociodemographic data for California's Bay Area, sourced from the Office of Environmental Health Hazard Assessment (OEHHA) (The Office of Environmental Health Hazard Assessment 2023), alongside 20,764 miles of local pavement condition data obtained from the metropolitan transportation commission (MTC) website [41]. The dataset contains 1584 census tracts and is used as a resource to explore which significant factors should be considered in the decision-making process for pavement management and define underserved areas. Table 2 summarizes the dataset used in the research. According to this dataset, 43% of the street pavement (length) in the Bay Area is in excellent condition, with a pavement condition index (PCI) of 80 or higher. Moreover, 28% falls under the category of good condition, with a PCI ranging from 79 to 60. Furthermore, 9% of the pavement is considered to be at risk, with a PCI between 59 and 50, while 20% is classified as poor condition, with a PCI of 49 or lower. Figure 2 represents the distribution of pavement condition across the California Bay Area. According to the sociodemographic data, the total population in the area is around 7,758,000, with only 7.5% of individuals over 25 years old having less than a high school education, almost 60% of the population are not classified as white, and almost 14% are aged 65 and older. The dataset was then analyzed to investigate any relationships between pavement condition and various factors, including educational level, traffic, air \begin{table}
\begin{tabular}{l l} \hline \hline
**Data** & **Description** \\ \hline \multirow{2}{*}{Pavement Condition} & 20,764 miles of local roads \\  & Source: Metropolitan Transportation Commission (MTC) website \\ \hline \multirow{2}{*}{Sociodemographic} & 1584 census tracts \\  & Source: Office of Environmental Health Hazard Assessment (OEHHA) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: California’s Bay Area data summary. Figure 2: Pavement condition distribution in California’s Bay Area. quality, race and ethnicity, age, income level, and population density. Initially, a non-parametric approach was employed to identify key factors influencing pavement quality in underserved areas. This exploratory phase prioritizes data-driven insights, allowing for a flexible analysis of efficiency without predefined weights. This approach is particularly well suited for uncovering complex relationships between various sociodemographic factors and pavement condition. A statistical analysis was initially conducted, followed by the application of the Lasso (least absolute shrinkage and selection operator) regression method for more precise predictions and a better understanding of the impact of these factors on pavement condition. The detailed results are presented in Tables 3 and 4 in the result section. The second step of analysis in this framework involves developing an optimization model that can solve Equation (1). This model for selecting maintenance and rehabilitation (M&R) options transitions to a parametric approach. The specific objective functions will be defined to quantify social equity, environmental impact, and economic efficiency. The weights for these objectives are determined exogenously, based on the established literature, policy considerations, and sustainability criteria. The proposed framework incorporates objective constraints and regulations into the optimization process to ensure the proposed solutions are not only optimal but also feasible within the context of existing regulations and practical limitations. These constraints are derived from federal and state-level guidelines, standards, and regulations related to social equity, environmental justice, and sustainability considerations in transportation asset management.