Road accidents constitute a significant proportion of the number of serious injuries reported every year. Yet, it is often challenging to determine which specific conditions lead to such events, making it more difficult for local law enforcement to address the number and severity of road accidents. We all know that some characteristics of vehicles and the surroundings play a key role (engine capacity, condition of the road, etc.). However, many questions are still open. Which of these factors are the leading ones? How much are the external factors to blame, compared to the driver skills?

We leveraged Machine Learning and the United Kingdom’s road accidents database to clarify these questions and specifically provide impact on two major areas:

1. First, we developed a risk score that quantifies the likelihood of a driver having a fatal/serious accident solely based on inputs gathered from individual and vehicle data. This score can be used both to influence driving rules and regulation and inform drivers on the factors that increase their accident risk.
2. Second, we analysed situational information (such as road type, weather conditions, etc.) to estimate the severity of an accident. Such insights would help governments to better understand the sources of accidents and act to reduce them.

Data

We use 220k+ accident reports from the Department for Transport of the United Kingdom, covering 2018. For each report, we have the information collected at the scene of the accident including:

• Casualty characteristics (e.g. gender, age and home area type)
• Situational variables (e.g. weather, road type and light conditions)
• Accident descriptors (e.g. severity, presence of police)
• Vehicle descriptors (e.g. age, power, type, model)

Overall, the data provided by the Department for Transport can be grouped into driver information, which can be further broken down into vehicle and individual data, and external information ( e.g._ accident location_ and light conditions).

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