# Predicting Road Traffic Accidents--Artificial Neural Network Approach
## 1 Introduction
#### Literature Review  
Predicting traffic accidents and understanding the factors that influence traffic safety has been the focus of numerous studies in various regions. These studies have employed different tools and methodologies to investigate the relationship between specific factors and the occurrence of accidents [8]. In the Highway Safety Manual, Chapter 10 explicitly addresses the prediction model for rural two-lane, two-way roads, emphasizing the impact of traffic volume (AADT) through safety performance functions (SPF) and project geometry and traffic management characteristics through crash modification factor (CMF) [9]. The major subject of the paper [10] was to determine which parameter has the greatest influence on the occurrence of traffic accidents. Several studies have examined the influence of specific factors on traffic accidents on two-lane rural roads. [PERSON] and [PERSON] [11] researched Minnesota and Washington to analyze the geometric characteristics leading to accidents. [PERSON] et al. [12] focused on the difference in traffic crashes between two-lane and four-lane highways in Texas. [PERSON] et al. [13] identified that head-on crashes were affected by AADT, percentage of trucks, and shoulder width. [PERSON] [14] developed an accident prediction model for curves and tangents on two-lane roads in Portugal. This research defined an accident prediction model for curves and tangents, as well as for roads with paved and unpaved shoulders. Two-lane rural roads were the main subject of [PERSON] et al. [15] research. In his research, a prediction algorithm was developed. [PERSON] et al. [16] analyzed almost 3500 km of two-lane rural roads in Spain (region of Valencia and West Castile) in a five-year period. The article by [PERSON] et al. [17] analyzed about 170 km of two-lane rural roads in Italy over a period of 5 years; in this paper variables such as: curvature (radius, length), tangent length, cross-section elements (lane width and shoulder width and type). Research on traffic accidents in Ghana was presented in [PERSON] and [PERSON] [18] paper. The model for prediction developed in this research was GLM (Generalized Linear Model) with a Negative Binomial error structure. In India, [PERSON] and [PERSON] [19] developed a model for accident prediction with random parameters. Variables for the model were: traffic volume (veh/h), length (km), percent of buses, trucks, cars, and two-wheelers, as well as shoulder width and curvature (horizontal and vertical). [PERSON] et al. [20] developed accident prediction models for two-lane rural roads in New Zealand. A total of 6800 km of state roads was analyzed. The developed model created a relationship between a number of accidents and traffic volume, road geometry, cross-section, road surfacing, roadside hazards, and driveway density. For different types of accidents was developed different linear models. A combination of three different statistical methods was developed in a paper written by [PERSON] et al. [21]. Models used were: (1) gamma-updating of the occurrence rates of injury accidents and injured road users, (2) hierarchical multivariate Poisson-lognormal regression analysis, and (3) Bayesian inference algorithms. Influence parameters were: traffic volume, percentage of trucks and buses, speed, curvature and the number of lanes. Other machine learning methods are attempted for traffic systems to aid in transportation planning, including LSTM/RNN often used for sequence modeling and prediction [22; 23], CNN used for image and video processing tasks [24; 25], and broader machine learning for traffic accident analysis [26]. Research in Malaysia conducted by [PERSON] et al. [27] analyzed 448 segments of state roads over five years. Input variables was: horizontal curvature, terrain type, heavy-vehicle percent, and access points Traditional crash prediction models, such as generalized linear regression model, are unable to take into account multilevel data design [28].