Assessment of Capacity Changes Due to Automated Vehicles on Interstate Corridors

About the report:

This study was designed to assess capacity changes due to the introduction of connected vehicles (CVs) and automated vehicles (AVs) on Virginia freeway corridors.  Overall, three vehicle types, including legacy vehicles (LVs); vehicles equipped with adaptive cruise control (ACC) (AVs); and vehicles equipped with cooperative adaptive cruise control (CACC) (connected automated vehicles [CAVs]), were considered in mixed traffic scenarios.  Each scenario included light-duty passenger vehicles and heavy vehicles (HVs) with AV and CAV capabilities to determine their overall effect on capacity. 

The team developed an AV and CAV driving behavior model and evaluated it on a test network.  According to the testing results, the 100% AV and 100% CAV scenarios increased road capacity by 28% and 92% over the 100% LV scenario, respectively, on a basic freeway segment with intermediate vehicle behavior.  Moreover, in the case of the HV scenario, AVs and CAVs showed a substantial capacity increase. Simulations were also conducted on models of I-95 in Virginia, where AVs and CAVs improved capacity compared to LVs. However, in some scenarios during congested conditions, AVs performed worse than LVs with reduced speeds and increased travel times because of the frequent stop-and-go conditions because of short headways.  This issue was mitigated with the implementation of CAVs because of their ability to communicate and increase string stability.  Under uncongested conditions, AVs and CAVs improved throughput and reduced delays as compared to LVs but caused a small decrease in speeds and an increase in travel times.  Additional simulations were performed on models of I-81 to test the effects of extended grades and high percentages of HVs, where AVs and CAVs were found to have a high potential of improving operations when compared to LVs.  The presence of steep grades negatively affected the performance of all types of vehicles, especially HVs, when compared to flat terrain.  CAVs with their communication capabilities, particularly at high market penetrations, were capable of achieving capacity increases over AV and LV scenarios in the selected I-81 segment. 

AVs and CAVs proved capable of improving highway operations. Even in the presence of high percentages of HVs and steep grades, vehicles equipped with AV and CAV technologies provided better performance than LVs.  Ultimately, AVs and CAVs need full market penetration to operate at their maximum potential.  However, these technologies, even in mixed traffic, could still offer operational benefits at lower penetrations.

The Virginia Department of Transportation’s (VDOT) Traffic Engineering Division should stay updated on developments in AVs to ensure that VDOT simulation models reflect the existing and anticipated vehicle fleet.  They should consider using the capacities described in this report as guidance when calibrating models of CVs and AVs in simulations of freeway corridors. Because capacity estimates depend on AV and CAV market penetration, VDOT and the Virginia Transportation Research Council should investigate methods to estimate the prevalence, capabilities, and rate of usage of CV and AV driving technologies on Virginia roads.

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