Impact of Connected and Automated Vehicles on Longitudinal and Lateral Performance of Heterogeneous Traffic Flow in Shared Autonomy on Two-Lane Highways

Intelligent transportation systems and connected and automated vehicles (CAVs) are advancing rapidly, though not yet fully widespread. Consequently, traditional human-driven vehicles (HDVs), CAVs, and human-driven connected and automated vehicles (HD-CAVs) will coexist on the roads for the foreseeable future. Simultaneously, car-following behaviors in equilibrium and discretionary lane-changing behaviors make up the most common highway operations, which seriously affect traffic stability, efficiency and safety. Therefore, it’s necessary to analyze the impact of CAV technologies on both longitudinal and lateral performance of heterogeneous traffic flow. This paper extends longitudinal car-following models based on the intelligent driver model and lateral lane-changing models using the quintic polynomial curve to account for different vehicle types, considering human factors and cooperative adaptive cruise control. Then, this paper incorporates CAV penetration rates, shared autonomy rates, and string intensity into a Markov chain model to represent heterogeneous traffic flow. To verify the comprehensive performance of CAVs, this paper introduces both theoretical derivations and numerical simulations. A generalized linear string stability criterion and a fundamental diagram model are conducted to evaluate CAVs’ impact on longitudinal performance. Theoretical results indicate that large-scale deployment of CAVs improves string stability and traffic capacity. Besides, a discretionary lane-changing scenario on two-lane highway, based on the MOBIL model, is established to assess CAVs’ overall performance. Simulations demonstrate that increasing CAV penetration rates enhance traffic speed, efficiency, ride comfort and safety across different traffic densities. Appropriately reducing the proportion of HD-CAVs also benefits the overall performance. A parameter sensitivity analysis further reveals that higher driver compliance rates significantly improve traffic flow while higher weighted coefficients of communication gain achieve better safety by sacrificing other performance. These findings underscore the substantial impact of CAV technologies on advanced mixed traffic flow and lay foundation for developing control strategies tailored to heterogeneous traffic flow on two-lane highways.