A multi-vehicle communication system to assess the safety and mobility of connected and automated vehicles

Abstract

Connected and automated vehicles (CAVs) are expected to improve both traffic safety and efficiency by reducing the human driver errors. Recently, many researchers have focused on the simulation-based studies in order to evaluate the benefits of CAVs due to the lack of real-world data. However, none of the previous studies have attempted to differentiate the benefits of CAVs over automated vehicles (AVs) by incorporating multiple preceding vehicle information (i.e., acceleration, position, etc.). This paper aims to fill the existing gap by utilizing separate car-following models for both CAVs and AVs in order to approximate their driving behavior in the Aimsun Next simulation platform. Additionally, a different car-following model is used for the connected vehicles (CVs) without automation by addressing the human driver compliance factor. This study also utilizes mixed penetration of CAV and CV with no automation. A well calibrated and validated simulation testbed is developed for SR417 in Orlando, Florida which is the base scenario in this study. To this end, the impact of CAVs, AVs, and CVs are evaluated based on both traffic efficiency (i.e., travel time) and safety (i.e., traffic conflicts) under various market penetration rates (MPRs). The traffic efficiency results show that travel time is significantly reduced for any MPRs of CAVs, AVs, and the mixture of CVs and CAVs compared to the base scenario. A generalized estimating equation (GEE) model is developed to quantify the travel time improvement for CAVs, AVs, and the mixture of CVs and CAVs. The results suggest at least 20% penetration is required for CAVs to get travel time improvement while 40% penetration is needed for AVs. Also, CAV significantly outperforms AV for the same MPRs. For the safety assessment, traffic conflicts are estimated by using different surrogate measures i.e., time-to-collision (TTC) and time exposed time-to-collision (TET). The results imply that crash risk is significantly reduced for CAVs, AVs, and the mixture of CVs and CAVs scenarios compared to the base condition. A Bayesian zero-inflated negative binomial model is developed in order to model the number of traffic conflicts as a function of MPRs of CAVs, AVs, mixture of CVs and CAVs, and traffic parameters. The results confirm that CAVs are more efficient in reducing crash risk compared to AVs for the same MPR. The mix penetration rate of CV (60%) and CAV (20%) shows almost similar reduction of crash risk with the 80% MPR of AV. Also, crash risk analysis based on different vehicle types shows that CAVs driving behavior is safer compared to the AVs. Finally, the results of this study indicate a significant improvement of both traffic efficiency and safety by implementing CAV with multivehicle communication system on the freeway segments.