# Equity in Transportation Asset Management: A Proposed Framework
## 3 Results and Discussion
Similarly, the poverty rate showed a coefficient of 0.1, supporting the observation that tracts with higher poverty rates tend to have a higher percentage of pavement in poor conditions. This implies that these two factors should be used as key indicators for defining underserved areas and should be incorporated as equity factors in the decision-making process. Decision-makers should consider these areas as underserved when allocating budgets or making decisions on M&R treatments if they want to establish an equitable management system. On the other hand, the coefficients of other factors, such as race and ethnicity, educational level, population density, and traffic were aligned with the statistical results, showing coefficients of 0, indicating no significant relationship between these factors and pavement conditions in the dataset. The only contradictory finding was related to the factor of age. While the statistical analysis indicated that areas with a percentage of people older than 65 years had no correlation with pavement condition, the Lasso regression showed a slight increase in the percentage of pavement in poor condition, with a coefficient of 0.01. This trend is further highlighted in Figures 4 and 5. Figure 4 demonstrates that tracts with higher poverty rates tend to have a greater percentage of pavement in poor conditions compared to areas with lower poverty rates. This indicates that these areas are more neglected or receive less attention for budget allocation for M&R projects, emphasizing the need for some equity adjustment in the decision-making process. Moreover, Figure 5 reveals that tracts with poor air quality mostly showed a higher percentage of pavement in poor condition compared to areas with better air quality. There are several possible explanations for these observations. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Poverty Rate** & **Air Quality** & **Traffic** & **Education Level** & **Race and Ethnicity** & **Population** & **Age** \\ \hline
**Coefficient** & 0.10 & 0.27 & 0.00 & 0.00 & 0.00 & 0.0 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Lasso regression method coefficient. An important factor that could explain these findings is the land use type of the tracts, whether they are industrial or residential areas. Typically, air quality in industrial areas is lower compared to residential areas due to increased emissions and pollutants associated with industrial activities. Therefore, any potential relationship between the land use type and pavement condition should be investigated in future studies. Consequently, there is a possibility that the land use type should also be considered as another factor in defining underserved areas during the decision-making process. Additionally, the poor pavement condition itself could play a significant role in environmental issues. The presence of distressed pavement leads to frequent interruptions and traffic delays, which result in increased fuel consumption, higher emissions, and carbon footprint, which is one of the most important contributing factors in environmental equity assessments. In conclusion, both the statistical analysis and Lasso regression results highlight air quality and poverty rate as the most influential factors for defining underserved areas, which might be considered in an equitable pavement management planning. The statistical analysis revealed that these factors have \(p\)-values less than 0.05, signifying their statistical significance. Moreover, the Lasso regression revealed non-zero coefficients, further supporting their importance in the decision-making process.