The use of dedicated lanes, known as managed lanes (MLs), on freeways is an established traffic management strategy to reduce congestion. Allowing automated vehicles (AVs) in existing MLs or dedicating MLs for AVs, referred to as AVMLs, has been suggested in the literature as a tool to improve traffic operation and safety performance as AVs and driver-operated vehicles (DVs) coexist in a mixed-vehicle environment. This paper focuses on investigating the safety impacts of deploying AVMLs on freeways by repurposing general-purpose lanes (GPLs). Four ML strategies considering different lane positions and access controls were implemented in a traffic microsimulation under different AV market adoption rates (MARs) and traffic demand levels, and trajectories were used to extract rear-end and lane change conflicts. The time-to-collision (TTC) surrogate safety measure was used to identify critical conflicts using a time threshold dependent on the type of following vehicle. Rates of conflicts involving different vehicle types for all ML strategies were compared to the case of heterogeneous traffic. The results indicated that the rates of rear-end conflicts involving the same vehicle type as the lead and following vehicle, namely DV-DV and AV-AV conflicts, increased with ML implementation as more vehicles of the same type traveled in the same lane(s). By comparing the aggregated conflict rates, the design options that were deemed to negatively impact traffic efficiency and capacity were also found to negatively impact traffic safety. However, other ML options were found to be feasible in terms of traffic operation and safety performance, especially at traffic demand levels below capacity. Specifically, one left-side AVML with continuous access was found to have lower or comparable aggregated conflict rates compared to heterogenous traffic at 25% and 50% MARs, and, thus, it is expected to have positive or neutral safety impacts.

Vehicles planning to exit at an upcoming freeway off-ramp require adequate exit distance to execute lane change maneuvers, otherwise, traffic disturbances may be experienced. This research assesses the changes in exit distances for a mixed traffic environment comprising automated vehicles (AVs) and human-driven vehicles (HDVs) on freeways with and without a managed lane (ML). A left-side continuous ML was designed and eligible vehicles were AVs. Traffic microsimulation exercises were conducted on a 3.5 km freeway segment, and scenarios varied based on traffic demand, the number of freeway lanes, and AV adoption rates. Traffic demand was set relative to the queue discharge flow rate ( q_Q). The results indicated an increase in q_Q as the AV adoption rate increased. Also, the exit distances were influenced by the traffic environment, the freeway configuration, and the traffic demand. The optimal exit distance increased when an ML was implemented at 25% and 50% AV adoption rates.

The introduction of autonomous vehicles (AVs) on roadways will result in a mixed vehicle environment consisting of these vehicles and manual vehicles (MVs). This vehicular environment will impact intersection sight distances (ISDs) due to differences in the driving behaviors of AVs and MVs. Currently, ISD design values for stop-controlled intersections are based on AASHTO’s guidelines, which account only for human driver behaviors. However, with AVs in the traffic stream, it is important to assess whether the existing MV-based ISDs are compliant when an AV is present at an intersecting roadway. Hence, this study utilizes the Monte Carlo Simulation method to compute the PNC of various object locations on the major and minor roadways for possible vehicle interaction types in a mixed vehicle environment at a stop-controlled intersection. Scenarios generated considered these variables and the major roadway speed limits and sight distance triangles (SDTs). ISD non-compliance was determined by examining the PNC metric, which occurred when the demand exceeded the supply. The results indicated that when AV–MV interaction was present at the intersection, the MV-based ISD design was non-compliant. However, it is possible to correct this non-compliance issue by reducing the AV speed limit.

As automated vehicles (AVs) are introduced into the traffic fleet, their operational differences from driver-operated vehicles (DVs) may impact traffic performance and safety. Researchers suggest that dedicated lanes could reduce AV–DV interactions. This paper presents a simulation-based study evaluating the performance of four ML strategies in mixed AV–DV traffic, considering lane position and access control, AV market adoption rate, and ML eligibility. Sixteen ML cases were optimized and compared to a heterogeneous traffic environment based on capacity and efficiency. The results indicated that traffic demand level, MAR, and ML strategy can impact freeway performance. A heterogeneous traffic environment was favoured in most MARs and traffic demand levels. However, one AV left-side ML can be deployed at MARs of 25% regardless of access control, or one AV left-side ML with continuous access at 50% MAR. Implementation of these strategies would involve updating roadway signage to ensure correct usage of the freeway.