This paper investigates the potential changes in the geometric design elements in response to a fully autonomous vehicle fleet. When autonomous vehicles completely replace conventional vehicles, the human driver will no longer be a concern. Currently, and for safety reasons, the human driver plays an inherent role in designing highway elements, which depend on the driver’s perception-reaction time, driver’s eye height, and other driver related parameters. This study focuses on the geometric design elements that will directly be affected by the replacement of the human driver with fully autonomous vehicles. Stopping sight distance, decision sight distance, and length of sag and crest vertical curves are geometric design elements directly affected by the projected change. Revised values for these design elements are presented and their effects are quantified using a real-life scenario. An existing roadway designed using current AASHTO standards has been redesigned with the revised values. Compared with the existing design, the proposed design shows significant economic and environmental improvements, given the elimination of the human driver.

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The rapid development of connected vehicle (CV) and cooperative automated vehicle (CAV) technologies in recent years calls for the assessment of the impacts of these technologies on system performance. Microscopic simulation can play a major role in assessing these impacts, particularly during the early stages of the adoption of the technologies and associated applications. This study develops a method to evaluate the safety benefits of red-light violation warning (RLVW), a CV-based vehicle-to-infrastructure (V2I) application at signalized intersections, utilizing simulation. The study results confirm that it is critical to calibrate the probability to stop on amber in the utilized simulation model to reflect real-world driver behaviors when assessing RLVW impacts. Without calibration, the model is not able to assess the benefits of RLVW in reducing RLR and right-angle conflicts. Based on a surrogate safety assessment, the calibrated simulation models result shows that the CV-based RLVW can enhance the safety at signalized intersections by approximately 50.7% at 100% utilization rate of the application, considering rear-end, and right-angle conflicts.

Assessment of the safety and mobility impacts of connected vehicles (CVs) and cooperative automated vehicle applications is critical to the success of these applications. In many cases, there may be trade-offs in the mobility and safety impacts depending on the setting of the parameters of the applications. This study developed a method to evaluate the safety and mobility benefits of the Stop Sign Gap Assist (SSGA) system, a CV-based application at unsignalized intersections, which utilizes a calibrated microscopic simulation tool. The study results confirmed that it was critical to calibrate the drivers’ gap acceptance probability distributions in the utilized simulation model to reflect real-world driver behaviors when assessing SSGA impacts. The simulation models with the calibrated gap parameters were then used to assess the impacts of the SSGA. The results showed that SSGA can potentially improve overall minor approach capacity at unsignalized intersections by approximately 35.5% when SSGA utilization reaches 100%. However, this increase in capacity depended on the setting of the minimum gap time in the SSGA and there was a clear trade-off between capacity and safety. The analysis indicated that as the minimum gap time used in the SSGA increased, the safety of the intersection increased, showing for example that with the utilization of an 8-s gap at a 750 vph main street flow rate, the number of conflicts could decrease by 30% as the SSGA utilization rate increased from 0% to 100%.

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