# A Predictive Pedestrian Crash Model Based on Artificial Intelligence Techniques
## 1 Introduction  
Pedestrian injuries and fatalities represent one of the major road safety problems worldwide. Due to the vulnerability of these road users, the consequences of accidents involving pedestrians show higher severity when compared with other kinds of accidents, and lead to a significant rate of morbidity, disability, and death. Estimates suggest that approximately 12 million road accidents involving pedestrians occur every year and cause the deaths of about 270,000 people worldwide (around 23% of all traffic fatalities globally [1]). This burden, in addition to inflicting pain and suffering on injured pedestrians and their families, also has a significant economic impact on society, costing approximately 0.5% of the total world Gross National Product and USD 130 billion globally [2]. In 2018, 4763 pedestrians' fatalities were reported in Europe, accounting for 20% of total traffic deaths [3]. Although the absolute number of pedestrian fatalities fell from 5952 to 4763 fatalities between 2010 and 2018 (\(-20\%\)), the total number of road fatalities decreased to the same extent (\(-21\%\)); therefore, the proportion of pedestrians in the total number of road fatalities is the same. Therefore, the proportion of pedestrians in the overall number of road fatalities has remained almost constant in recent years [3]. In Italy, the situation is even more serious, with about 20,000 road accidents with at least one pedestrian occurring every year. According to the Italian data published by ACI-ISTAT [4], in 2019, 534 pedestrians were killed and more than 21,000 were injured intraffic-related crashes. Out of these, about 30% occurred at pedestrian crossings, which is the location where pedestrians should be the most protected while crossing the street. The safety of pedestrians, especially in urban areas where there is a higher density of pedestrians present, should be a priority to improve people's quality of life and to support pedestrians' mobility, and in general, the mobility of vulnerable road users. The European Commission highlighted the need to define criteria to improve the safety and mobility of vulnerable road users. This also requires a framework that takes the various needs of vulnerable road users into account. Concepts such as Sustainably Safe Traffic and Zero Vision provide the framework for infrastructure planning to drastically reduce the probability of crashes. However, effective infrastructural countermeasures to make walking safer and protect pedestrians require an understanding of the risk factors affecting pedestrians' safety and the causes of accidents involving pedestrians [5], especially in areas where roadside and pedestrian activities, traffic volume, and traffic speed variations are significantly higher than in other road environments [6]. In this context, the roadway safety management process plays an important role in improving traffic safety. Within this topic, Accident Prediction Models (APMs) represent one of the best available tools for performing a quantitative safety assessment. APMs are mathematical equations that allow road engineers and/or National Road Authorities (RAs) to relate the number of crashes expected on a site to its specific geometric and environmental characteristics. Nowadays, APMs are widely used by road authorities, designers, and road safety practitioners to investigate safety issues and estimate the potential safety effects of these improvements [7; 8]. APMs also allow locations that may benefit the most from a safety treatment to be identified, and therefore, for strategies and priorities to be better defined [9]. The Highway Safety Manual (HSM) is currently the most widely recognized method in accident prediction research [10]. It provides a predictive method for estimating the expected average crash frequency (divided by crash severity and collision types) within the road network.