# Equity in Transportation Asset Management: A Proposed Framework
## 5 Conclusions  
In the world of infrastructure development, asset management in a sustainable manner could affect people in different communities, environments, and economic growth. However, achieving sustainable asset management has many challenges when trying to balance economic, environmental, and social equity objectives. Balancing the economic, environmental, and social aspects of pavement management involves making careful choices. On the economic front, efforts to enhance social and environmental equity may necessitate significant initial financial investments. For instance, initiatives like constructing new roads in underserved areas or electrifying vehicle fleets to mitigate environmental impact can strain already limited transportation budgets due to high upfront infrastructure and operating costs. However, certain pavement management strategies demonstrate that economic goals need not always conflict with environmental and social equity objectives. These approaches often yield both environmental and social benefits at lower financial costs. Policies solely focused on social equity in pavement management may result in overall efficiency losses, potentially leading to greater environmental impacts, higher user costs, and strained budgets for agencies. Nevertheless, well-designed investments targeting traditionally underserved groups have the potential to unlock productivity gains and catalyze economic growth. This research aimed to develop a prototype decision-support framework for allocating budgets in transportation asset management projects, covering all three equity aspects simultaneously. The focus was on pavement maintenance, rehabilitation, and reconstruction projects. The framework proposed in this study consists of three steps. The first step was defining the research objective, followed by some analysis to investigate key factors in defining underserved areas based on a case study dataset, and then developing an optimization model and finalizing by evaluating the model with some metrics. The optimization model aims to minimize the assets' condition gap between underserved areas and the rest of the network, minimize greenhouse gas emissions, and maximize road network conditions. The case study was a comprehensive dataset for California's Bay Area, containing sociodemographic and local pavement condition data. The analysis of this dataset revealed that areas with different poverty rates and air quality experience varying pavement conditions. Areas with higher poverty rates and worse air quality tend to have a higher percentage of pavement in poor condition. The Lasso regression method also provided more precise results, confirming the positive correlation between poor air quality and higher poverty rate with the percentage of pavement in poor condition. Furthermore, it revealed a slight increase in poor pavement conditions in areas with an older population. These results highlight that these factors should be considered in the decision-making process to establish an equitable pavement management system. The findings indicate that land use type might also be an important factor to consider in decision-making process conditions and should be investigated in further studies. Moreover, it shows that poor pavement condition itself might contribute to environmental issues by causing traffic disruptions, increased fuel consumption, and higher emissions, influencing environmental equity assessments. In conclusion, this study's integrated decision-support framework offers valuable insights for sustainable pavement management. It should be noted that the correlation between poor pavement conditions and impoverished socioeconomic conditions, as observed in the reviewed data, may not universally apply. Factors such as geographical location, economic development, and cultural influences could potentially modify the relationship between pavement conditions and socioeconomic conditions.