Contemporary research indicates that the era of autonomous vehicles (AVs) is not only inevitable but may be reached sooner than expected; however, not enough research has been done to address road infrastructure readiness for supporting AV operations. Highway agencies at all levels of governments seek to identify the needed infrastructure changes to facilitate the successful integration of AVs into the existing roadway system. Given multiple sources of uncertainty particularly the market penetration of AVs, agencies find it difficult to justify the substantial investments needed to make these infrastructure changes using traditional value engineering approaches. It is needed to account for these uncertainties by doing a phased retrofitting of road infrastructure to keep up with the AV market penetration. This way, the agency can expand, defer, or scale back the investments at a future time. This dissertation develops a real options analysis (ROA) framework to address these issues while capturing the monetary value of investment timing flexibility. Using key stakeholder feedback, an extensive literature review, and discussions with experts, the needed AV-motivated changes in road infrastructure were identified across two stages of AV operations; the transition phase and the fully-autonomous phase. For a project-level case study of a 66-mile stretch of Indiana’s four-six lane Interstate corridor, two potential scenarios of infrastructure retrofitting were established and evaluated using the net present value (NPV) and ROA approaches. The results show that the NPV approach can lead to decisions at the start of the evaluation period but does not address the uncertainty associated with AV market penetration. In contrast, ROA was found to address uncertainty by incorporating investment timing flexibility and capturing its monetary value. Using the dissertation’s framework, agencies can identify and analyze a wide range of possible scenarios of AV-oriented infrastructure retrofitting to enhance readiness, at both the project and network levels.

<div>This research analyzes over 60,000 awarded highway contracts from 18 states throughout the United States. Analysis was performed on the state and aggregate level. The contracts were awarded from the years 2008 through 2018. Statistical analysis utilizing Pearson's Correlation and Ordinary Least Squares regression for each sample was performed to identify each variables relationship between the budget and awarded values.</div><div>The research examined effects of economic indicators, contractor descriptors and yearly/seasonal adjustments These variables included DBE Participation Goal, Number of Bidders, Project Dollar Value, Project Duration, Unemployment Rate, S&P 500 Index, Volatility Index, quarter, and year of project award. The results were examined by using a combination of simple statistical summaries and econometric coefficients called a cost vector. <br></div><div>Summary statistics observed Bid Difference at 8.5% below the Engineer's Estimate. The study observed DBE Participation Goals averaged 3.74% of the value of contracts, with an observed average of 4.5 bidders per contract. <br></div><div>The research determined that 55% of observed states had a positive significant correlation with DBE Participation Goal and Bid Difference. This correlation translated to nearly $80 million in additional cost. In addition, the research determined that all 19 groups in this study had a negative significant correlation with the Number of Bidders. The correlation translated to a savings of nearly $500 million. <br></div>