A data-calibrated microscopic simulation framework for quantifying mobility–environment trade-offs of connected and automated vehicles at freeway merge areas

During the long transition period toward full automation, however, connected and autonomous vehicles (CAVs) will coexist with Human-Driven Vehicles (HDVs), resulting in heterogeneous traffic conditions whose operational and environmental impacts remain insufficiently understood. This study proposes a data-calibrated microscopic simulation framework for evaluating the mobility and environmental impacts of CAV deployment under mixed traffic conditions. Comprehensive simulation experiments are conducted by considering five CAV market penetration rates, three automation levels represented by desired time headways, and three freeway speed-limit strategies. The calibration results demonstrate that the optimized Intelligent Driver Model (IDM) substantially improves behavioral realism. Simulation results indicate that increasing CAV penetration significantly enhances traffic efficiency, reducing average time loss, shortening travel time, and increasing average speed under a fully automated scenario. Additionally, reducing freeway speed limits moderately improves environmental performance with only marginal losses in traffic efficiency. Finally, CAV deployment inevitably increases energy cost, especially when CAVs are powered by electricity.