Environmental impact estimation of mixed traffic flow involving CAVs and Human-driven vehicles considering the non-equilibrium state

The impact of Connected and Autonomous Vehicles (CAVs) on the mixed traffic of CAVs and Human-driven Vehicles (HVs) has been extensively investigated. Existing network-level research predominantly emphasizes the improvement of road capacity by CAVs, often overlooking their influence on other critical aspects, particularly traffic instability. However, traffic instability is an important yet undesirable feature of traffic flow, where small perturbances grow into stop-and-go waves, resulting in higher emissions. This study addresses this gap by focusing on the enhancements of CAVs on road capacity and traffic stability and estimating their environmental impact. To this end, we develop an improved Dynamic Network Loading (DNL) model for mixed traffic by expanding the discrete form of a velocity gradient model to road networks. The variances in road capacity, shockwave speed, and propagation speed of a small disturbance, arising from the interactions between CAVs and HVs, are considered. Numerical studies showcase the capabilities of this model in several aspects: Firstly, it successfully reproduces stop-and-go waves and illustrates a gradual alleviation of the waves with an increasing penetration rate of CAVs. These are beyond the ability of discretized Lighthill-Whitham-Richards (LWR)-type models, like the Cell Transmission Model (CTM). Moreover, it provides a more accurate estimation of vehicle speeds and emissions compared to the CTM, with simulation results benchmarked against SUMO. Furthermore, this research explores the relationship between CAV penetration rate and aggregate network emissions, using a dynamic user equilibrium model. Our findings reveal that higher CAV penetration rates correlate with reduced emissions. Specifically, a complete transition to 100% CAVs yields a substantial reduction in network emissions, estimated at approximately 20%.