With the development of Autonomous Vehicles (AVs), a promising future for their implementation becomes increasingly apparent. However, it is essential to acknowledge that the effects of AV deployment are not straightforward, particularly when considering scenarios involving AVs from different manufacturers and various levels of automation. The current research predominantly concentrates on human-driven vehicles(HDVs) and AVs. However, the assumption that AVs exhibit homogeneous behavior is a simplification that does not reflect the actual diversity within this category. This research is undertaken to evaluate the impact of autonomous vehicle heterogeneity on traffic flow. To assess AV heterogeneity, the initial step involves an examination of the manifestations of AV heterogeneity through data analysis. The data source of the data analysis includes two parts, the Adaptive Cruise Control (ACC) data, and the high-level AV data. For the ACC data, the open ACC dataset is used. As for the high level, the processed Waymo and Lyft 5 datasets are used. These datasets encompass essential information, including the position, acceleration, and speed of the vehicles within the platoon, which is instrumental in identifying and characterizing heterogeneous driving behaviors. The analysis focuses on analyzing parameters such as Time-to-Collision (TTC), time gap, and acceleration/deceleration patterns. As for the time gap, the investigations include the distribution of time gaps under different speed ranges and different acceleration conditions. The results of the analysis contribute to the conclusion that heterogeneity among AVs is evident, not only across various automation levels but also within the same level of AVs. Given the presence of heterogeneity, characterized by the same or different automation levels with differing behavioral patterns among AVs, the car-following models are employed to capture this heterogeneity. Therefore, these parameters are calibrated using a genetic algorithm and maximum likelihood estimation is applied to determine the best-fit distributions of desired time gaps and maximum accelerations. Calibrated car-following models are then employed to represent the longitudinal behaviors of AVs. The parameters are drawn from distributions, it is expected that AVs will exhibit slightly varying behaviors. To assess the impact of heterogeneous traffic on traffic flow, various scenarios are constructed and evaluated. The scenarios encompass ACC vehicles, HDVs, and combinations of ACC, highly automated vehicles(HAVs), and HDVs. The first scenario aims to assess the impact of heterogeneity among AVs of the same automation level, so the different shares of ACC vehicles are involved. In contrast, the second scenario involves HAVs and ACC vehicles to evaluate the influence of heterogeneity arising from various AV automation levels. The ultimate conclusion drawn from this study suggests that heterogeneity negatively impacts traffic efficiency. Specifically, the efficiency gains afforded by vehicles equipped with ACC are offset by the presence of heterogeneous traffic at low penetration rates. Furthermore, the results obtained from simulation scenario 2 indicate that the introduction of multi-level AVs may have a detrimental effect on traffic efficiency and stability. These findings underscore the need to validate and improve AV performance comprehensively before embarking on large-scale implementation efforts.

This research is carried out to determine the effect of truck platooning on traffic flow using empirical data. This research contains two parts, data fusion, and statistical analysis. For data fusion, loop detector data, infrastructure information and weather data will be added to the original data set. For statistical analysis, the time gap distributions under different categories are analyzed to determine the performance of the truck platoon. Additionally, an analysis of the lane change behavior is included.