Evaluating the Impact of Human-Driven and Autonomous Vehicles in Adverse Weather Conditions Using a Verkehr in Städten—SIMulationsmodell (VISSIM) and Surrogate Safety Assessment Model (SSAM)

Advanced driving technologies have the potential to transform the transportation sector. Specifically, the progress of autonomous vehicles (AVs) has caught the interest of governmental authorities, industrial groups, and academic institutions, with the goal of improving the driving experience, effectiveness, and comfort while also improving safety and flexibility and lowering vehicle emissions. Considering these facts, the purpose of this study is to assess the possible effects and advantages of AVs under diverse traffic situations in urban and rural environments. Knowledge of traffic behavior inside a certain road network is made easier by traffic microsimulation. PTV VISSIM (Verkehr In Städten—SIMulationsmodell) is among the microsimulation software programs that has attracted great interest because of its remarkable capacity to faithfully simulate traffic conditions. This review helps researchers choose the best methodological strategy for their individual study objectives and restrictions while using VISSIM. This research assesses the effect of AVs in different driving behavior and weather conditions in urban and rural situations using VISSIM and introduces traffic safety using the surrogate safety assessment model (SSAM). The study focuses on 10 parameters from the Wiedemann 99 car-following model and speed distribution to establish the correlation between weather conditions and surrogate safety measures (SSMs). The findings could lead to more accurate and authentic models of driving behavior and encourage the automotive industry to further equip AVs to operate efficiently in various environmental and driving conditions.