## 3 Experiments and Results
### Discussions  
When preprocessing and extracting behavioral features, we handled video footage on an end-point server with direct access to the image feed from individual CCTV cameras (see Figure 3). Because we only worked with two cameras and processed one hour of footage from each day, our system could afford to run more slowly than real-time. However, it is possible to optimize the system to run at or faster than real-time, which will be necessary to expand the scale to larger urban areas with more cameras available, as well as longer daily study periods. The future goals of this study are to accumulate a large number of vehicle-pedestrian interaction events, in diverse environments and external conditions, and provide a dense dataset for other types of behavioral analysis. Much research has been conducted on pedestrian safety evaluation at locations where they are vulnerable (e.g., mid-block crosswalks, intersections, school zones, etc.) by analyzing vehicle and pedestrian behaviors and their interactions based on vision sensors. Table 5 compares characteristics of previous systems with our approach. To assess potential risk, these systems extracted various standardized features from video footage, such as time-to-collision (TTC) or post-encroachment time (PET), but in practice, most relied on manual effort by a human observer. The closest approach to  
Figure 15: Examples of objects’ states based on rules in (**a**) cluster #0; (**b**) cluster #1; (**c**) cluster #2; (**d**) cluster #3; (**e**) cluster #4; and (**f**) cluster #5.  
ours was [40], which similarly automated the entire process of object detection, tracking, and feature extraction, but focused on TTC at signalized intersections. By contrast, our process focused on unsignalized crosswalks, looked more generally at velocities and distances to assess risk, and introduced clustering as a new way to categorize types of risk event. Thus, we combine the advantages of automated feature extraction with machine learning to efficiently characterize potential risk events in these vulnerable locations. In addition, by classifying potentially risky situations, we sought to create an objective metric that planners could use to compare different crosswalks, based on the relative frequency of these events at each location. Crosswalks dominated by \"dangerous\" clusters demand closer attention than those with mostly \"safe\" events. However, as our results show, there are still clusters of situations that, while distinct, are not easily classified as \"safe\" or \"dangerous\". Because vehicle-pedestrian interactions happen many times more often than actual accidents, this approach provides a richer, denser, more consistent perspective on the safety of these environments. We hypothesize that a much larger-scale PPRE dataset could actually be correlated with accident rates to clarify the relative risk of different types of event, or even predict accident rates based on observable driver/pedestrian behavior at the crosswalks. This could become part of a decision support system allowing transportation engineers or city planners to study existing areas, propose alternative road/intersection designs, and test the impact of these physical changes over relatively short timeframes. We plan to develop this decision support system in subsequent studies.