# Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies
## 7 Emerging Research Areas and Future Directions
### Safety Implications of Connected and Autonomous Vehicles  
To ensure the effective deployment of autonomous vehicle (AV) technologies, it is crucial to account for both public perception and the underlying safety challenges highlighted by real-world accident data. While [PERSON] et al. [123] underscore the public's optimism regarding the potential of AVs to reduce crash frequency and severity, their findings also emphasize significant apprehensions about system failures, cybersecurity risks, and privacy concerns. They analyzed public perceptions of autonomous vehicles (AVs) by applying a grouped random-parameter bivariate probit model with heterogeneity in means. Based on a survey of 584 U.S. respondents, the study found that while 66% and 68% expected fewer and less severe crashes, respectively, significant concerns existed regarding equipment failure in poor weather (71%) and potential crashes due to system failures (73%). Furthermore, 68% of respondents worry about hacking and terrorist attacks, while 74% express concerns about privacy breaches. The study highlights the importance of continuously monitoring these perceptions for effective AV deployment strategies. In the same vein, [PERSON] et al. [129] discussed solutions to traffic safety regarding autonomous vehicles (AVs) through two main objectives: identifying patterns in traffic accidents and developing test scenarios for AVs based on these patterns. The authors analyze the STATS19 accident data, a dataset of 20,000 accidents from the UK, using the COOLCAT clustering algorithm, which is designed for high-dimensional categorical data. This analysis reveals six distinct clusters of traffic accidents, each characterized by unique real-world situations, aiding in the understanding of risk factors. Additionally, the study employs association rule mining to create non-trivial test scenarios for AVs, addressing the industry's challenge of ensuring safe deployment in risky situations. The findings show the value of clustering techniques and more effective data collection methods to inform safety strategies for emerging vehicle technologies, contributing to safer transportation systems. These findings by [PERSON] et al. [129] underscore the importance of addressing complex accident patterns and developing tailored safety strategies for autonomous vehicles. Expanding on this focus, [PERSON] et al. [124] transitioned to real-world crash data from California, offering valuable insights into specific collision types and the environmental factors influencing AV-including accidents. Together, these studies highlight the critical need for both predictive safety frameworks and practical interventions to enhance the safe integration of AVs in diverse traffic environments. Their research concludes that while automated vehicles (AVs) in California have accumulated significant mileage, the insights gained from analyzing crash reports reveal critical patterns in AV-including accidents, particularly the high frequency of rear-end collisions (61.1%). The study emphasizes the need for careful consideration of unobserved heterogeneity in crash data, advocating for the use of informative uniform priors in Bayesian models over thetraditional uninformative inverse-gamma priors. The findings suggest that environmental factors, such as mixed land use and proximity to schools, play a significant role in crash propensity. Practical implications include the potential for enhanced rear-end collision avoidance through the implementation of automatic emergency braking systems in conventional vehicles, which could lead to improved safety outcomes in mixed traffic scenarios involving both AVs and human-driven vehicles. Similarly, [PERSON] et al. [125] delved into the dynamics of mixed traffic scenarios, shedding light on how intelligent and connected vehicles (ICVs) influence traffic flow stability and capacity. They analyzed the traffic flow configurations and the spatial distributions of various types of vehicles when mixed traffic flow is in equilibrium.