AbstractThe use of predictive models to investigate the performance of automated network management (ANM) under multiple control strategies and given traffic demands will help assess control options in a connected and automated vehicular environment without testing them in a simulation environment. The current prediction models used to evaluate the traditional networks are not applicable in an automated environment. The goal of this study is to develop delay and surrogate conflict predictive models to examine the performance of a reservation-based intersection control strategy in a flexible automated network. Efforts have been made to identify the correlation between the delay and the total number of conflicting requests with types and characteristics of the conflict points and conflicting flows. To generate an extensive origin-destination level dataset, three combined flexible lane assignment and reservation-based intersection control (CFALRIC) models were used in which the through traffic is shared in six various proportions with one or both turning lanes and tested under two different traffic demands, leading to a total of 36 scenarios. The selection of appropriate input variables was a crucial step in this study. Linear regression and multigene genetic programming (MGGP) approaches are utilized to derive new prediction models for the delay and the number of conflicting requests. Results reveal that the regression model is a reasonably appropriate fit for both models; however, the accuracy of the MGGP approach is higher for the delay prediction model. These models have great potential to be applied for planning purposes such as traffic assignment in an automated environment.