# Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies
## 7 Emerging Research Areas and Future Directions
### Big Data Analytics and Data Mining Techniques  
[PERSON] et al. [65] developed a genetic mining rule model utilizing a stepwise rule-mining algorithm. Their study integrated 29 mined rules into a mixed logit model to identify key safety and risk conditions associated with severe crashes. Their analysis revealed that seat belt fastening was the most critical safety condition, while risk conditions included vehicle type, alcohol use, driver characteristics, time period, road status, and surface condition. These findings demonstrate the effectiveness of a two-stage mining  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Research Area** & **Key Methodological Contributions and Findings** & **References** \\ \hline Big Data Analytics and Data Mining & Two-stage mining framework integrating 29 mined rules into mixed logit model; identifies seat belt fastening as most critical safety condition; capture of joint effects of risk factors in single-vehicle freeway crashes. [PERSON] et al. [65] \\ \hline Deep Learning and Advanced AI Applications & Comparative analysis shows simpler models often achieve performance comparable to or better than that of deep models; random forest models are the most effective for crash risk prediction using crowdsourced probe vehicle data. [PERSON] et al. [127]; [PERSON] et al. [128] \\ \hline Real-Time Crash Risk Prediction & Hybrid LSTM-CNN model with parallel structure captures long-term dependencies and local features; it achieves the highest AUC of 0.93, highest sensitivity and the lowest false alarm rate for urban arterial prediction. [PERSON] et al. [67] \\ \hline Connected and Autonomous Vehicle Safety & Survey of 584 U.S. respondents reveals 66–68\% expect fewer and less severe crashes; concerns include equipment failure in poor weather (71%), system failures (73\%), hacking (68\%), and privacy breaches (74\%). [PERSON] et al. [123] \\ \hline Autonomous Vehicle Crash Pattern Analysis & COOLCAT clustering algorithm identifies six distinct accident clusters from UK STATS19 data; a total of 61.1\% of AV-including accidents are near-end collisions; environmental factors like mixed land use and school proximity influence crash propensity. [PERSON] et al. [129]; [PERSON] et al. [124] \\ \hline Intelligent Connected Vehicle Traffic Flow & Mixed traffic flow analysis shows ICVs improve stability under critical speeds and enhance traffic capacity; stability degrades when critical speed exceeded; critical speed decreases as maximum platoon size increases. [PERSON] et al. [125] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of emerging research areas and key findings.  
framework in capturing the joint effects of risk factors contributing to single-vehicle crash severity on freeways.  
The application of advanced data mining techniques, as exemplified in the study, provides a robust foundation for identifying intricate, multi-dimensional relationships in traffic safety, thereby informing more effective crash prevention strategies.