Microscopic Simulation Model for Mixed Traffic of Connected Automated Vehicles and Conventional Vehicles on Freeways

Abstract

This study developed mixed-traffic simulation models of connected automated vehicles (CAVs) and manually-driven vehicles (MDVs) at the full-spectrum of mixed penetration rates on a freeway segment by incorporating the car-following and lane-changing models via a conditional linkage to investigate the sensitivities in highway capacity and travel time. The car-following models for CAVs and MDVs were modified from the full-velocity difference (FVD) car-following model, while the lane-changing logic was adopted to regulate the lane-changing decisions for both CAVs and MDVs. The desired speeds of each MDVs were determined on the basis of stochasticity to represent various desired speeds taken by human drivers, while the uniform desired speed was employed for CAVs. The stochastic gap acceptance was applied for MDVs to replicate the stochasticity of the gaps accepted by human drivers, whereas the static gap acceptance was adopted to establish the safe decision-making thresholds for CAVs prior to performing lane changes. Two algorithms were proposed separately for governing the movements of CAVs and MDVs in the traffic simulation models. The proposed algorithms, along with a 3-to-2 virtual freeway lane drop, were coded in JAVA to develop a simulation platform, prior to calibrating the default model with field data. Eleven mixed traffic scenarios were simulated in the developed platform, along with parallel simulation in VISSIM, to generate and validate the resultant speed-flow diagrams. The results were then analyzed and compared to determine the changes in highway capacity and travel time with respect to the variations in CAV penetration rate. The resultant vehicular trajectories in the scenarios of interest were also analyzed to perceive the impact of CAVs on the trajectories and speeds of the interacting vehicles in traffic. The results showed increase in capacities in the range of 25.9 – 26.9 percent, while travel time decreased by up to 55.4 percent, as the CAV penetration rate shifted from 0 to 100 percent. The trajectory analysis indicated that CAVs have an influence on guiding the smoother speed and acceleration rates of MDVs while an MDV is following a CAV. The results suggest that although headways increased with increasing CAV penetration rate, capacity also increased; however, there should be an optimal headway that maximizes the capacity.