# A Predictive Pedestrian Crash Model Based on Artificial Intelligence Techniques
## 2 Data and Methods  
### Data  
#### Data Overview  
The ISTAT database provides data related to 874,847 road accidents during a 5-year period (2014-2018) according to the HSM approach [24]. Out of these, 652,367 road accidents happened in an urban area and 101,030 involved at least one pedestrian (on average more than 20,000 per year), thus representing 11.5% of the total number of serious and fatal road accidents.  
Here, only this category was taken into account. Between the last year of the analysis (2018) and the first (2014), a 3.80% decrease in road accidents with at least one pedestrian involved was observed.  
The dataset was analyzed in terms of gender, age, and road environment characteristics. The results showed that male drivers represent about 75% of the drivers involved. On the other hand, more female pedestrians obtained injured than males. The age class range with the highest number of pedestrians injured and killed is 65+, probably due to the pedestrians' fragility.  
The road characteristics were grouped by road type, road section type, signs type, road pavement type, and surface condition. Urban roads represent the road type with the highest number of accidents. Within this category, crashes represent about 88% of the total accidents. The most common type of road section with a high rate of pedestrian accidents is the straightaway (about 63% of the total), where vehicles pass by at a high speed, followed by roads including crossings (14% of the total). Concerning the location of the road accidents, the presence of both vertical and horizontal signs is prevalent, and the vast majority of the accidents occurred on paved roads (as urban roads in urban areas are usually paved in Italy) on conditions of dry or wet surface (82%). These data can be interpreted as the result of reduced pedestrians' presence in cases of adverse weather conditions, and this should be considered in light of the overall mild climate of Italy.  
From the ISTAT dataset, it is also possible to retrieve the kind of accident and the behavior of both the drivers and pedestrians. Figure 1 shows the interaction between driver and pedestrian behavior. The ten most common behaviors for drivers and pedestrians are considered and a ten-by-ten grid is displayed. Each tile represents the interaction between one driver's and one pedestrian's behavior. The color of the tile is proportional to the number of accidents for a specific interaction. The closer the color is to red, the higher the number of accidents that occurred is.  
Figure 1: Number of accidents for each pedestrian–driver interaction.  
It is worrying to note that 29.3% of the road accidents with at least one pedestrian involved occurred when the pedestrians crossed the street at a pedestrian crossing without the protection of traffic lights or a traffic warden. It is of the utmost importance to outline this because the type of crossing (the so-called \"zebra crossing\") where the accidents happen is the default crossing type in Italy. Additionally, the pedestrian crossing is the location where the pedestrian should be protected the most.  
Table 2 summarizes the main findings of the data analysis of road accidents with at least one pedestrian involved.  
Given these findings, it is likely that speeding represents a key factor in causing accidents, although the relationship between speed and crashes remains one of the most debated topics in traffic safety [25; 26; 27; 28; 29; 30; 31].