Book excerpt: Traffic crashes impose a significant socio-economic cost on societies. According to the World Health Organization (WHO), 1.2 million people die every year, and more than 50 million people are injured due to fatal and non-fatal crashes globally. Safety concerns are more serious on rural corridors that play crucial roles in freight movement, such as Interstate 80 (I-80) in the State of Wyoming. Being affected by Wyoming’s adverse weather conditions, high altitude, challenging geometric characteristics, and critical traffic composition, there has been a notable crash and critical crash rate on 402-miles of this major freight corridor in Wyoming. To alleviate these safety concerns, the United States Department of Transportation Federal Highway Administration (USDOT FHWA) selected the Wyoming Department of Transportation (WYDOT) to deploy a Connected Vehicle (CV) Pilot Program along I-80 in Wyoming (WYDOT CV Pilot). The WYDOT CV Pilot focuses on the needs of the commercial vehicle operator and will develop CV applications to support a flexible range of services under Vehicular Ad-hoc Network (VANET), including roadside alerts, parking notifications, and dynamic travel guidance. In this regard, evaluation of the safety impacts of the CV Pilot is central to the USDOT’s strategic goals. The literature pointed out that the Market Penetration Rate (MPR) of CVs should be large enough to ensure safety and operational benefits of CVs. However, at early stages of the WYDOT CV Pilot, CVs will be contributing to a small fraction of the entire traffic stream, challenging traditional safety performance evaluation methodologies to assess the effectiveness of the CV technology. With these concerns, a comprehensive Analysis, Modeling, and Simulation (AMS) framework in addition to reliable baseline Analyses are required to scrutinize the safety performance of CVs under various MPR. These requirements have been fulfilled in this research through the use of advanced statistical modeling, Machine Learning, Deep Learning, data mining techniques, data visualization, and taking practical advantages of simulation- and driving simulator-based analyses. In the developed baseline and under the concept of Real-Time Risk Assessment (RTRA), significant real-time traffic-related variables contributing to crash and critical crash occurrences on the 402-miles I-80 in Wyoming during CV pre-deployment were identified. Using advanced statistical modeling and data visualization tools provided by Machine Learning techniques, the causal effect of these significant factors on the crash/ critical crash probabilities were explored. These causations are expected to be affected due to CV technology under notable MPRs in the future. Accordingly, the conducted baseline will be used as a benchmark against explored crash causations during CV post-deployment to grasp how this technology alleviates or changes the causality patterns, revealing the WYDOT CV Pilot safety performance. Furthermore, based on the preprocessed real-time traffic observation from the RTRA, the research calibrated and validated a reliable AMS framework to assess the safety effectiveness of the WYDOT CV Pilot that mainly goes around level-0 and level-1 of automated driving systems. At these levels, drivers are in charge of the execution of steering, acceleration/deceleration, and monitoring of the driving environment; thus, the human factor contributing to more than 90% of traffic crashes is still in that safety loop. Having said that, the AMS framework primarily aims to show how various CV applications, designed under WYDOT CV Pilot, would alter CV drivers’ behavior under traffic critical safety events and measure the effect of this alteration on I-80 traffic safety performance. Accordingly, drivers' behavioral alterations due to CV notification were quantified under the concept of with/without analysis and in a series of comprehensive high-fidelity driving simulator experiments conducted at the University of Wyoming Driving Simulator Lab (WyoSafeSim). These quantifications were analyzed separately and were conflated with traffic microsimulation modeling to reveal the safety effects of CV technology on the I-80 traffic stream under varying CV MPRs. This dissertation's findings and insights would be of interest to the WYDOT, the USDOT FHWA, and practitioners in the safety domain. The provided crowd-sourced real-time traffic dataset in the conducted baseline would help the WYDOT in understanding the current safety performance of I-80, identifying black-spot points in high-risk I-80 segments, and developing proactive countermeasures and interventions for Active Traffic Management (ATM) to alleviate the risk of traffic crashes on this major freight corridor. The data-driven crowdsourcing procedure performed on the AMS framework would shed some light on realizing the impact of CV technology on enhancing drivers’ situational awareness and minimizing the rate of motor vehicle crashes, which is not limited to I-80 in Wyoming. The integration of a high-fidelity driving simulator with traffic microsimulation modeling, as a two-pronged approach applied in the AMS framework, would show a fruitful pathway for the safety performance assessment of other CV pilots deployed by the FHWA with small CV MPRs at early deployment stages. Besides, beyond the main scope of assessing CV applications designed for WYDOT CV Pilot, the developed AMS framework could be utilized to evaluate the safety effect of other CV applications, such as the application of CV Variable Speed Limit (VSL) on lengthy rural corridors for the sake of spatiotemporal speed harmonization. The developed Road Weather Connected Vehicle Applications AMS framework was further extended by incorporating driver behavior and performance in adverse weather conditions utilizing a comprehensive Naturalistic Driving Study (NDS) dataset from the second Strategic Highway Research Program (SHRP2). The developed AMS framework could be helpful for a wide array of safety and operations of the next generation active traffic management.