## 4 Results and Discussion
### Data Quality Improvement and Application of Results
#### 4.3.1 Generating New Pedestrian Crash Features  
The CNN models were instrumental in extracting additional pedestrian crash attributes that were previously unavailable in structured crash datasets. These newly generated features enhance the depth and accuracy of pedestrian crash analysis, allowing for a more comprehensive understanding of crash patterns and associated risk factors.  
Two key extracted variables include roadway type (divided vs. undivided) and intersection type (three-leg vs. four-leg). These attributes are fundamental in analyzing pedestrian crash dynamics, as roadway configurations and intersection layouts directly impact pedestrian safety. The CNN models successfully extracted and classified these features, ensuring greater accuracy in assessing pedestrian crash risks across different roadway and intersection types. This improvement provides researchers and transportation agencies with more precise data for evaluating infrastructure design and its influence on pedestrian crashes.  
Another crucial feature is the presence of marked crosswalks, which significantly impact pedestrian safety. Marked crosswalks designate pedestrian crossing areas and influence driver behavior, making them essential for evaluating crash risks at intersections and midblock locations. Identifying whether a crash occurred at a marked crosswalk helps researchers and transportation agencies assess pedestrian exposure risk and develop targeted countermeasures, such as enhanced crosswalk markings, signal timing adjustments, or pedestrian refuge islands.  
Additionally, the study extracted Michigan left turn presence--a unique traffic maneuver commonly used in Michigan to regulate left-turn movements. While designed to improve traffic flow and reduce vehicle crashes, its impact on pedestrian safety remains a critical area of research. By classifying crash diagrams based on the presence of Michigan left turns, this study enables a better understanding of pedestrian-vehicle interactions at these locations, informing potential modifications to enhance pedestrian safety.  
The presence of adjacent residential areas was also extracted, as proximity to residential zones significantly influences pedestrian activity levels. Residential areas typically generate higher pedestrian volumes, increasing the risk of crashes, especially in locations with inadequate pedestrian infrastructure. Including this feature allows for a more detailed assessment of how land use and roadway design interact with pedestrian crash occurrences, guiding urban planners in implementing traffic-calming measures and improving pedestrian facilities in high-risk residential zones.  
Another key feature identified was intersection skewness, which refers to the angle at which roads intersect. Skewed intersections can affect visibility, pedestrian crossing behavior, and driver decision-making, increasing the likelihood of pedestrian crashes. Extracting this variable enables safety analysts to identify hazardous intersection geometries and propose design modifications, such as improved signage, adjusted signal timings, or the realignment of skewed intersections to enhance pedestrian safety.