This research project delves into the intricacies of road traffic accidents severity in the UK, employing a potent combination of machine learning algorithms, econometric techniques, and traditional statistical methods to analyse longitudinal historical data. Our robust analysis framework includes descriptive, inferential, bivariate, and multivariate methodologies, correlation analysis: Pearson's and Spearman's Rank Correlation Coefficient, multiple and logistic regression models, Multicollinearity Assessment, and Model Validation. In addressing heteroscedasticity or autocorrelation in error terms, we've advanced the precision and reliability of our regression analyses using the Generalized Method of Moments (GMM). Additionally, our application of the Vector Autoregressive (VAR) model and the Autoregressive Integrated Moving Average (ARIMA) models have enabled accurate time-series forecasting. With this approach, we've achieved superior predictive accuracy, marked by a Mean Absolute Scaled Error (MASE) of 0.800 and a Mean Error (ME) of -73.80 compared to a naive forecast.

For many years, the capacity of predicting the future was reserved to few people and their tools were limited to crystal balls, hand palms and tarot cards. But for the last 50 years, new tools have emerged and forecasting is now accessible to many more people and this is great! In this article, I will show you how to perform basic timeseries forecasting on a simple example. We will analyze, visualize and forecast road accidents in Switzerland using the open-source library Facebook Prophet and a LSTM neural network using Keras / Tensorflow. The jupyter notebooks I used for this article are available on my github.

Traffic accidents are extremely common. If you live in a sprawling metropolis like I do, chances are that you've heard about, witnessed, or even involved in one. Because of their frequency, traffic accidents are a major cause of death globally, cutting short millions of lives per year. Therefore, a system that can predict the occurrence of traffic accidents or accident-prone areas can potentially save lives. Although difficult, traffic accident prediction is not impossible.

Most road accidents are caused by drivers' fatigue, poor driving judgement or speeding - with a driverless car this risk of this happening reduces. With the rise in artificial intelligence and technological revolutions, Minister of Health Chris Fearne noted that driverless cars are the right step towards increasing safety on the roads. Speaking during a Road Safety Conference in collaboration with WHO, held on The World Day of Remembrance for Road Traffic Victims, Fearne said that road traffic accidents are one of the highest causes of injury and death within the age groups 15-40. In The World Health Organization (WHO) region, in one hour 10 people die in road traffic accidents. Fearne said that such road accidents are preventable with the use of law enforcement and educational campaigns.

Research recently conducted for The Times has presented a fairly negative snapshot of the public's perception of autonomous vehicle technology. In it, almost two thirds of motorists said they would not buy a driverless car, suggesting that people don't trust driverless technology… yet. Clearly, those involved in the burgeoning industry face an enormous challenge in reassuring those unnerved by the idea of not being in control. Failure to do so will see driverless vehicles join the scrap heap of failed transport modernisation projects. Safety is naturally top of the list when it comes to the prospect of driverless vehicles.