# Predicting Road Traffic Accidents--Artificial Neural Network Approach
## 1 Introduction  
Road traffic accidents represent one of the leading causes of death worldwide. According to the World Health Organization (WHO) [1], almost 1.3 million people die in road traffic accidents, while almost 50 million suffer non-fatal injuries. The leading cause of death of children and young people between 5 and 29 years is road traffic crashes. The serious problem is that 93% of fatalities come from the low-income and middle-income countries in development. Except for human suffering, road traffic accidents significantly influence the economy, costing countries up to 3% of their Gross Domestic Product (GDP). The occurrence of traffic accidents can be related to a variety of factors. Some of these factors are subjective: driver knowledge, training level, experience, the influence of alcohol, drugs, etc., while others are objective, such as: road volume (annual average daily trafficvolume--AADT), road geometry (curvature, slopes, lane width, shoulder width), type of road (freeway or two-lane road), road conditions (pavement quality and potential damage of pavement surface), weather conditions (wind, ice, snow, rain, etc. ), cars maintenance, speed limits, frequency of police controls, etc. An increase in a number of road vehicles leads to heavier traffic volume on almost all roads, which increases the chances of collision between vehicles. On the other hand, the same traffic volume on the freeway and the two-lane roads has a different possibility of collision. This variety of factors makes complicated efforts to predict the number of traffic accidents for new roads or reconstruction of existing roads. In order to predict the number of traffic accidents in the near future on the existing or the newly planned/built road section, it is necessary to know what factors will mostly influence the occurrence of a road traffic accident. Objective factors that cannot be easily changed in the future are recognized as: the type of road, road geometry and AADT (which will likely to be increased in the future). Other factors can be changed (pavement can be repaired, weather conditions are subject to daily change, car maintenance can be improved by frequent checks, speed limits can also be adapted, etc.). The classical machine learning models such as: artificial neural network (ANN), random forest regression (RFR), support vector machine (SVM), extreme learning machine (ELM), K-nearest neighbors (KNN) and decision tree (DT) are extensively used in modelling in various branches of science. The SVM is a widely used discriminant technique based on the statistical learning theory, well recognized for its strong generalization ability. The optimal network is obtained by exploring the balance between the complexity of the model and the training error [2; 3]. The ELM designs a single-layer feedforward network by randomly generating the input weights and biases of the hidden layers [4]. The vast variety of state-of-the-art machine learning techniques is suitable for sequence data like ensemble learning models, such as: XGBoost [5] and LightGBM [6] and CatBoost. XGBoost model exerts its advantages, especially for high prediction accuracy and interpretability. LightGBM model enables large amounts of data and GPU training. The LightGBM models are proven to be more accurate and faster than XGBoost. Furthermore, data fusion enables stronger forecasting accuracy, according to the integration of gradient boosting-based categorical attributes supported by the CatBoost algorithm [7]. Applying artificial neural networks has proved its feasible feature in recent years by predicting and presenting desired results although limited data sets. Obtained results illustrate that the variables such as highway width, head-on collision, type of vehicle at fault, ignoring lateral clearance, following distance, inability to control the vehicle, violating the permissible velocity, and deviation to the left by drivers are the most influential aspects that can raise traffic accidents in urban roads.