# Equity in Transportation Asset Management: A Proposed Framework
## 3 Results and Discussion  
In the first step of the analysis, the Shapiro-Wilk test was conducted to investigate whether the dataset followed a normal distribution. The Shapiro-Wilk test is a hypothesis test designed to assess whether a dataset follows a normal distribution. This test examines data from a sample, operating under the null hypothesis that the dataset is normally distributed. A high \(p\)-value suggests that the dataset conforms to a normal distribution, whereas a low \(p\)-value indicates a departure from normal distribution. According to the results (Figure 3), the data did not follow a normal distribution. Therefore, non-parametric tests were employed for further analysis. As mentioned in the methodology, the dataset used in this study contains a wide range of potential factors, including age, educational level, poverty rate, race and ethnicity, population density, traffic, and air quality that could impact pavement condition. The Kruskal-Wallis test was utilized to analyze any significant relationships between the aforementioned factors and pavement conditions. This non-parametric test was selected due to the normality assumption of the dataset is violated. The Kruskal-Wallis test does not require data to be normally distributed, ensuring a more robust analysis. Results are presented in Table 3. \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 \(p\)**-value** & 0.001 & 0.0114 & 0.489 & 0.141 & 0.495 & 0.523 & 0.491 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Kruskal–Wallis test result. Figure 3: Histogram and normal QQ plot of pavement condition data. According to the results, the \(p\)-value for the poverty rate and air quality is less than 0.05, indicating a statistically significant relationship between these factors and pavement conditions. This suggests that the pavement condition may vary in areas with different rates of poverty and varying air quality. In other words, there is a significant difference in pavement conditions between areas with varying poverty rates and air quality levels. This highlights the importance for decision-makers to consider these factors in their management process to promote equity in their pavement management system. However, factors such as population density, traffic, education, percentage of non-white population, and age, with \(p\)-values greater than 0.05, did not show a significant relationship with pavement condition in this specific dataset, which contradicts the findings in the Government Accountability Office (GAO) report [38]. This finding could be due to the fact that some agencies in the Bay Area have been considering race and ethnicity in their pavement management systems since 2017 [42]. However, further analysis needs to be conducted in individual areas to evaluate the effectiveness of that equity consideration. Currently, the Bay Area is being analyzed as a single data source. The results have also revealed a concerning observation regarding areas with higher poverty rates and areas with different ranges of air quality. There are several machine learning methods for feature selection, with Lasso regression being one of the most common ones [9]. It can select useful features while discarding useless or redundant ones. In Lasso regression, discarding a feature will set its coefficient equal to 0. Therefore, in the next step, the Lasso regression method was applied to the dataset to achieve more precise results and determine the type of correlation, whether it is positive or negative. The results are shown in Table 4.  
According to the results, air quality showed a coefficient of 0.27, indicating a positive relationship with the percentage of pavement in poor condition. This suggests that areas with worse air quality correspond to a higher percentage of pavement in poor condition.