A Predictive Pedestrian Crash Model Based on Artificial Intelligence Techniques

Abstract

Every year in Italy, there are about 20,000 road accidents involving pedestrians, with a significant number of injuries and deaths. Out of these, about 30% occur at pedestrian crossings, where pedestrians should be protected the most. Here, we propose a new accident prediction model to improve pedestrian safety assessments that allows us to accurately identify the sites with the largest potential safety improvements and define the best treatments to be applied. The accident prediction model was developed using the ISTAT dataset, including information about the fatal and injurious crashes that occurred in Italy in a 5-year period. The model allowed us to estimate the risk level of a road section through a machine-learning approach. Gradient Boosting seems to be an appropriate tool to fit classification models for its flexibility that allows us to capture non-linear relationships that would be difficult to detect via a classical approach. The results show the ability of the model to perform an accurate analysis of the sites included in the dataset. The locations analyzed have been classified based on the potential risk in the following three classes: High, medium, and low. The proposed model represents a solid and reliable tool for practitioners to perform accident analysis with pedestrian involvement.

Modeling road accident fatalities with underdispersion and zero-inflated counts

“…Logistic regression models have been seen in the literature [ 36 – 38 ], along with a range of machine learning techniques, such as regression trees, random trees, random forests, support vector machines, k-nearest neighbors, and deep neural networks [ 39 – 44 ]. In addition to count data and categorical data, there is considerable interest in the traffic congestion rate.…”

Towards Safer Roads: Predicting the Severity of Traffic Accident in Montreal using Machine Learning

“…In the study by [24], researchers address the pressing issue of pedestrian safety in traffic accidents, particularly in Italy. Every year in Italy there are approximately 20,000 traffic accidents involving pedestrians, with a significant proportion (approximately 30%) occurring at pedestrian crossings, where pedestrians should ideally be most protected.…”

Drivers' performance assessment approaching pedestrian crossings through the analysis of the speed and perceptive data recorded during on-field tests

“…An analysis of the Italian historical accident data (2014–2018) conducted by Meocci et al, in 2021 [ 7 ] revealed that 30 % of traffic collisions involving pedestrians occur at pedestrian crossings, which should be the safest place for pedestrians that are crossing the street. Therefore, it is necessary to seriously understand the factors affecting the phenomenon and provide effective solutions that reverse the trend and mitigate this issue worldwide.…”

“…A possible approach to research this topic is the passive approach, which is based on the analysis of crash database in order to identify of both crash-prone locations and factors affecting the phenomenon [ 7 , [21] , [22] , [23] , [24] ]. However, this approach fails to go into detail and does not investigate the crash environment or the road characteristics approaching the site of the crash [ 25 ] and often does not consider driving speed as a variable.…”