Understanding driver behaviour is essential to creating a safer roadway system as it has been always a major concern for transportation engineering professionals. According to the National Highway Traffic Safety Administration, human error leads to 94% of serious crashes in the United States. Equipped connected vehicles supplemented with crash warning systems are implemented to support motorists in their driving tasks and help minimize human errors. Failure to investigate driver interactions with these technologies could lead to suboptimal system design and non-compliance, resulting in increased risks of collisions and injuries. This thesis studies the effects of implementing a connected cruise control (CCC) equipped vehicle supplemented with a crash warning system on driver behaviour and road safety using a driving simulator. More specifically, this study will (i) examine the impacts of different message designs, connectivity configurations, and demographic on driver behaviour-related variables, (ii) model driver’s ratio of change in average speeds between connected and non-connected road sections, (iii) evaluate the safety performance of implementing CCC-equipped vehicles and identify those who have the potential to be involved in rear-end collisions (i.e., tailgaters), (iv) investigate the contributing factors to rear-end collisions in a connected environment using matched case-control logistic regression, and (v) classify tailgaters using the relative risks of case-control pairs and the survival function of the COX regression. The in-house driving simulator, called HyperDrive, at the Department of Civil Engineering, the University of Calgary was employed in conducting the simulation experiments. The simulation design considers specific variables of multiple categories. These variables are message content, delivery type, and vehicle-to-vehicle connectivity length. The simulation environment consists of an urban road with two lanes per direction and a posted speed limit of 80 km/ hr. This layout has two separate connectivity conditions, a non-connected road section (base condition) and a connected road section where each has a multi-vehicle collision scene. Message broadcasting is only activated on the connected road section. A call for participation was announced for those who are > 18 years old and hold a valid driver’s license with at least one year of driving experience, and in good health condition. Out of the 43 recruited participants, only 40 were considered for the analysis as the other 3 experienced simulator sickness and could not continue the experiment - one (male participant) of the three persons completed one run only. Multiple analyses were conducted to study the impacts of implementing the CCC from different perspectives. First, the effects of different crash warning system message designs, vehicle-to-vehicle connectivity configurations, and participants’ demographics were tested using one- and two-way ANOVA to observe any associations with driver behaviour-related variables. Second, ordinal logistic regression was used to model drivers’ choice to reduce their speeds while driving behind the CCC-equipped vehicle. Third, the road safety performance of implementing the CCC was assessed using four surrogate safety measures - the standard deviation of speed, the standard deviation of headway time, minimum TTC, and maximum braking -before and after implementing the CCC-equipped vehicle. Also, tailgaters were identified at different connectivity lengths, using a safety measure called Rear-End Accident Risk Index (REARI). tailgaters. Finally, matched case-control logistic regression was performed to predict and classify rear-end collisions because of driving behind the CCC-equipped vehicle. Key findings were observed out of the performed analyses. It was found that scenario variables had an impact on driver behaviour (e.g., lower average speeds and smoother deceleration rates after receiving either message design). Also, participants with bachelor’s degrees and more than 10 years of driving experience tend to reduce their speeds behind the CCC vehicle. A similar effect was observed at longer vehicle-to-vehicle connectivity lengths. In addition, lower variations of speed and headway time mean values were observed on the connected road section. Finally, more than 90% of rear-end collisions were truly classified using matched case-control logistic regression. This study demonstrates that implementing CCC and crash warning systems can have a positive impact on driver behavior and road safety. The results suggest that designing effective crash warning system messages and considering driver demographics can enhance the benefits of these technologies. Additionally, the study identifies several surrogate safety measures that can be used to assess the potential for rear-end collisions and highlights the importance of driver characteristics in predicting and preventing conflicts. These findings have significant implications for the design and implementation of future connected vehicle technologies.