# A Predictive Pedestrian Crash Model Based on Artificial Intelligence Techniques
## 3 Results and Discussion
Considering the definition of recall (Equation (1)) and precision (Equation (2)), this means that the occurrence of high-risk class predictions is very low for observations of low and medium risk classes (very low rate of HLPH and HMPH). However, only 71% of the high-risk class observations are correctly predicted (29% of false negatives) showing a tendency to underestimate the risk level. Concerning the low-risk class observations, 88% of the low-risk observations are correctly predicted (12% of false negatives), showing a tendency to overestimate the risk level. These two tendencies are confirmed by the relatively low precision value for the medium-risk class: Only 61% of the observations predicted as to be in the medium-risk class are correctly classified, which also confirms that the observations misclassified are only rarely severely misclassified (OLPH and OHPL). These results might be due to the heterogeneous distributions of observations on the dataset (60 observations, low risk; 95, medium risk; and 25, high-risk class). From the fit model, it is possible to retrieve information related to the importance of the independent variables. In Figure 6, the normalized importance (the importance of all the variables summed up to 100) of the independent variables used in the models is displayed. The variable with the highest importance (importance = 29%) is the total number of lanes in the section, showing a strong correlation between the complexity of a road section  
Figure 6: Normalized importance for the independent variables of the model. and the risk level. This result confirms the finding of the Abojaradeh studies conducted in Jordan on pedestrian bridges [41], where it is shown that one of the main factors affecting pedestrian safety was the width of the crossway. According to the results described in the international literature [42; 43], the other most important variables are those related to the traffic level during the morning, the afternoon, and the evening and the presence of a traffic light (importance equals 16%, 14%, 11%, and 14%, respectively). The findings previously mentioned also showed that a 5% decrease in traffic volume during peak hours, measured in terms of Passenger Car Units (PCUs), will result in an 11% decrease in total pedestrian accidents [43]. In a recent USA study, bus stop presence has an importance value near 5%, confirming that there is a significant spatio-statistical correlation between a bus stop location and pedestrian-involved crashes [44]. The main findings of our research showed that the variables mentioned (the number of lanes, traffic conditions, traffic lights, and bus stops) together already account for 89% of the total importance. Each of the other variables has an importance value of less than 5%. As said above, Figure 6 shows that the variable related to the presence of road bumps has an importance value equal to 0, but this value is due to the fact that all of the locations in the ISTAT database do not have road bumps to slow down the vehicles' speed. From these results, it seems that the complexity of a section and its traffic is the most important component in determining the accident risk level of a section for pedestrians. To reduce the risk of road accidents involving pedestrians, particular attention should be paid to the number of lanes in a road section and the traffic level of the section. Both factors should be taken into consideration in the design phase of the road section. In the case of an existing road section, changing the number of lanes or modifying its traffic level requires a big intervention on the entire traffic network and the effect of the operation on other road sections should be considered. The results obtained in the modelling phase can be considered satisfactory given the relatively small dataset (180 observations) and the limitations previously mentioned. However, multiple improvements could be implemented to achieve higher precision and recall scores and to ensure the higher reliability and robustness of the model.