# Predicting Pedestrian Crashes in Texas' Intersections and Midblock Segments
## 5 Results and Discussion
The distance to the nearest hospital is also analyzed; the coefficient is found to be negative and significant at a confidence level of 95%. This suggests that the number of crashes is lower in areas with hospitals located at a close distance, which is comparable to the finding of higher crashes in denser areas. The sensitivity analysis indicates that one standard deviation of distance (approximately 5 miles) leads to a decrease of 11% (intersections) and 5% (midblock segments) in pedestrian crashes. However, it is important to mention that although the number of crashes is low, the distance to the hospital can be critical for response time and prompt injury treatment. The presence of public transportation is an indirect measure of pedestrian exposure. In this study, transit information is included in the model through two variables. The first one is an indicator variable of the presence of transit within a buffer of 0.25 miles from the geometry centroid, and the second one is the count of transit stops in this area. As expected, both variables indicate that the number of pedestrian crashes is higher in the presence of transit, with a significant level of 95%. The sensitivity analysis suggests that the crash rate is about 50% higher in areas with transit stops in comparison to areas with the same characteristics without transit stops. The Texas model includes a variable to indicate the location of the city of Austin and compare crashes with the rest of the state. Interestingly, the number of intersection crashes in Austin is higher than in the rest of Texas, but the number of midblock crashes is lower. Similarly, Figure 1 suggests that the number of intersection crashes in Austin is comparable to the number of midblock crashes, mainly in recent years (after 2017). The Austin-specific model in Table 5 shows an intersection model that is less sensitive to walking density (WMT/area) and signalized intersections than the Texas model but still shows a significant value. It is likely that pedestrian crashes in non-signalized intersectionsare more frequent in this area compared to the state of Texas. The number of approaches also shows a positive coefficient. In terms of midblock segments, the number of intersections crossed is not significant, possibly due to the size of the segments. In this case, the segments are 0.1-mile long, and the number of intersections crossed is significantly lower than the case where 1-mile segments were used in the Texas model. DVMT has a positive significant effect on the number of crashes in the city of Austin. However, the sensitivity analysis suggests that the effect is less than the results obtained from the Texas model. One standard deviation increase in DVMT led to a 40% (intersection) and 72% (midblock) increase in the number of pedestrian crashes. The effect of the speed limit is only significant for the midblock segments and not for the intersections model, and the sensitivity change is comparable to the rest of the state. The number of lanes also has a positive and significant coefficient. Variables, such as lane width, median width, and one-way roads, are not significant at a 95% confidence level for the intersections model (in both cases, major and minor approaches). In terms of the traffic attributes, AADT is positive and significant for the intersection model (same as Texas). Still, the percentage of trucks is negative in both models (as opposed to the positive effect for intersections in Texas). This suggests that pedestrians in Austin are less likely to be involved in an accident at intersections with a high number of trucks compared to the rest of the state. The functional class indication for arterial roadways is not significant, and no conclusion can be obtained from this variable. In comparison to the Texas model, the city of Austin model also suggests that on-system roads have fewer crashes than other roadways.