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• A new disaggregated approach is provided for assessing the distributional equity impacts of AVs. • The feasibility of the approach for analyzing the distributional equity impacts of AVs was demonstrated by applying it to a case study in the Tampa Bay region, U.S. • Planning implications are provided through the case study findings. Better understanding of the equity impacts of automated vehicles (AV) is needed to design equitable deployment plans for AV technology. The goal of this study was to develop and demonstrate a modeling framework to support distributional equity assessments of AV systems prior to their wide adoption. This modeling framework takes a disaggregated approach that integrates agent-based simulation, diverse transportation outcomes, and equity analyses. The framework uses individual-level data to capture detailed disparities in transportation outcomes, applies a state-of-the-art multi-agent traffic simulation model (MATSim) to simulate privately-owned and shared AVs simultaneously, and considers distributional equity from multiple perspectives. To test and demonstrate the framework, a case study was conducted for the Tampa Bay region. Five scenarios were considered with different AV market shares and integration strategies based on scenario planning by the U.S. Federal Highway Administration. Results reveal that high AV penetration rates were required for substantial reductions in inequality. The introduction of AVs led to a more even distribution of accessibility, but slightly more uneven distribution of traditional mobility and affordability. Impacts of disparities in outcomes for disadvantaged groups were mixed. These results suggest that AVs will likely perpetuate existing inequity in the transportation system as long as its fundamental structure remains the same as today. Results highlight the importance of planning and design strategies that directly address the distributional impacts to ensure that AV technology is deployed equitably. [ABSTRACT FROM AUTHOR]

• A novel multistep cooperative mandatory platoon lane change (CMPLC) strategy is proposed for CAV platoon lane changes in mixed traffic. • Three trajectory planning models are formulated to allow CAVs to quickly create acceptable gaps and complete lane changes. • A quantitative evaluation of the impact of CAV platoon lane changes on HDVs in the target lane is conducted. • The locations of the lane change critical points are identified in different scenarios, serving as warning indicators for CAV platoons. This study presents a Cooperative Mandatory Platoon Lane Change (CMPLC) strategy for Connected and Autonomous Vehicles (CAVs) navigating from a Dedicated Lane (DL) to an adjacent General-Purpose Lane (GPL) with Human-Driven Vehicles (HDVs). The CMPLC strategy involves two lane change steps and a gap widening step. Initially, a safety criterion for lane changes is proposed, integrating the merits of the Intelligent Driver Model (IDM) and the Gipps model, to constrain the gaps that the CAVs in the platoon can safely move into. Guided by this criterion, the strategy identifies the largest gap in the GPL near the platoon as the target gap in the first step. A subset of CAVs in the platoon performs rapid lane changes into the target gap, avoiding collision with HDVs. The following gap widening step employs a trajectory planning model based on Model Predictive Control (MPC) to widen two gaps in the GPL while considering the uncertain behavior of HDVs. Simultaneously, guided by another two trajectory planning models, the remaining CAVs in the DL sequentially move into their target gaps during the second lane change step. To validate the proposed strategy, a visual simulation platform integrating SUMO and MATLAB was developed. Numerical experiments were conducted on a two-lane highway featuring a DL on the inner side and a GPL on the outer side. The experimental results show that the proposed CMPLC strategy can always achieve higher lane change success rate and shorter execution time compared to the Individual Vehicle Lane Change (IVLC) strategy, under various settings of traffic demand, platoon size, and starting location. Additionally, through numerous simulation experiments, the critical lane change points for CAV platoons in different scenarios are identified, which can serve as early warning indicators for CAV platoons to avoid unsuccessful lane changes. [ABSTRACT FROM AUTHOR]

• Reliability evaluation of V2V traffic states using measurements from onboard sensors. • Reliable cooperative string stable control for CAVs under communication cyberattacks. • String stability proof for CACC system considering falsified states. This study proposes a reliable longitudinal distributed control strategy for connected and automated vehicles (CAVs) in the presence of communication cyberattacks. The proposed strategy is designed to utilize dependable measurements from onboard sensors to evaluate the reliability of vehicle-to-vehicle (V2V) information and enhance the platoon string stability in an integrated manner. Specifically, this paper proposes a real-time reliability evaluation mechanism for the ego vehicle based on sensing and communication information from multiple predecessors, employing an upper-tailed chi-squared test. The mechanism is then incorporated into a distributed multi-predecessor linear feedback controller to adaptively weigh the preceding vehicles' information, ensuring robustness against cyberattacks and maintaining the string stability of the vehicular platoon. To better understand the whole framework, sufficient conditions of true string stability and pseudo string stability are theoretically derived, unveiling the disturbance amplification jointly impacted by measurement accuracy of onboard sensors, cyberattack severity in V2V communication, and control parameters. Numerical experiments are conducted to validate the controller across various types of communication cyberattacks. The results suggest that the proposed control strategy can significantly alleviate the impact of cyberattacks and simultaneously dampen traffic oscillations effectively. [ABSTRACT FROM AUTHOR]