In the field of transportation engineering, there has been a shift towards implementing Connected Vehicle (CV) Technology as a means of improving transportation systems. This approach is becoming increasingly important due to limited space, high delay on roadways, and significant crashes. The CV technology is expected to be the most effective solution for making transportation systems more functional and safer, as it enhances drivers' decision-making abilities and helps to control traffic flow.To assess the impacts of CV technology, simulations and closed-course testing have been conducted. In addition, some pilot studies have been carried out in urban settings, where the goal is to achieve "zero deaths." However, a comprehensive understanding of the applications of Connected Vehicles in rural settings is necessary, as driver behavior can be unpredictable and location-dependent.This research aims to evaluate the performance of four CV applications in a rural environment: Red Light Violation Warning (RLVW), Pedestrian in Signalized Crosswalk (PEDINXWALK/ PEDPSM), Curve Speed Compliance (CSPDCOMP), and Speed Compliance in work zones (SPDCOMPWZ). There were no work zones in the study area hence analysis on SPDCOMPWZ was not included in this study. Though the research had four CV applications but each driver had only three applications installed in their vehicle. Hence the study obtained 4 different groups for all 3-paired CV applications.The study analyzed the impact of these applications on drivers' behavior and their reactions to evaluate the performance of CV applications. The analysis focused on drivers' speeds since speed happens to be one of the primary traffic parameters that can provide in-depth information on driver’s behavior on the road. The driver’s speed is analyzed once they receive a warning or trigger prior to a potential violation of these specific traffic rules; 1.Running red-light, 2. Conflict/ potential crash between vehicle and pedestrian in a signalized crosswalk, 3. Over-speed on an exit ramp and 4. Over-speeding in a work zone. Their speeds are observed prior to a trigger, 2.5 seconds after, and 5 seconds after receiving a trigger/ warning from the CV applications to prevent traffic violations and improve safety.The research found that all of the CV applications studied had a positive impact on drivers' behavior, as they slowed down significantly when there is a trigger. In order to further understand the “compliance” to the CV applications, a month-by-month assessment was performed. Essentially, this analysis of was undertaken to observe trends in how drivers performed over the duration of the study. However, there was no significant change in the drivers’ behavior with respect to the month or season. Group assessments were also performed for the same reason. Essentially, to observe trends in how drivers performed given a different pair of CV applications. This is where drivers experiencing same CV application but in different group are compared to examine any different response to trigger. These analyses also yielded no significance, thereby concluding that drivers behave the same towards a particular CV application irrespective of the CV application combinations in their vehicles.To prioritize among the CV applications (RLVW, PEDPSM, CSPDCOMP), the study considers effect size and crash reduction rates. The RLVW application emerges as the primary focus due to its substantial effect size and a remarkable 15% reduction in crashes resulting in all injuries. Following closely, CSPDCOMP gains priority with a moderate effect size and an average 11% crash reduction rate for all injuries. PEDPSM is assigned a lower priority owing to its smaller effect size and the absence of a significant crash reduction.These findings demonstrate the potential effectiveness of CV technology in improving transportation systems' safety and functionality, particularly in rural environments. In the future, researchers should consider a control group, where participants have the CV applications installed but not active to the participants, this will help to know whether participants follow the CV warning to make safe driving decisions or when they feel unsafe.