A multitude of externalities affects transport efficiency and numbers of trips. Population expansion, urban development, political issues, fiscal trends, and growth in the field of connected, automated, shared, and electric (CASE) vehicles have all played prominent roles. While the market is keenly aware of the upcoming shift to the CASE vehicles, the transformation itself is reliant upon the development of technologies, customer outlook, and guidelines. The purpose of this research is to establish an overview of the possible network design problems, as well as potential consequences to vehicle automation systems by employing machine learning and system dynamics analysis. Finally, the cost of the required highway expansion for the critical links in the traffic network will be predicted. First, model was created for calculating traffic flow activity and necessitated highways to consider the impact of CASE vehicles between 2021 and 2050. Second, an economic evaluation outline was created to calculate optimum time and roadway improvement scenarios by a cost-prediction model using machine learning. Florida's interstate highways were employed as the subjects for the case study. The research showed that non-linear models had a better ability in the estimation of traffic flow, while linear models were better predictors of highway construction cost. These results also showed new technologies would add to traffic flow and capacity, with the increase in flow outpacing the increase in capacity. The consequences of this would be the level of service (LOS) of the current infrastructure decreasing. This study's results can assist discussion at the national and local level between government, networkers, automotive companies, tech-providers, logistics companies, and stakeholders for whom the practicality provided by the transportation infrastructure is crucial. This allows executives to create effective guidelines for subsequent transportation networks, ultimately accelerating the CASE vehicle network rollout to increase our current road network's level of service.
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Doctor of Philosophy (Ph.D.)
College of Engineering and Computer Science
Civil, Environmental, and Construction Engineering
Length of Campus-only Access
Doctoral Dissertation (Open Access)
Mahdavian, Amirsaman, "Machine Learning Algorithms for Forecasting the Impacts of Connected and Automated Vehicles on Highway Construction Costs" (2022). Electronic Theses and Dissertations, 2020-. 1049.