In the Netherlands, a frequently used mode of transport is the bicycle. A quarter of the daily trips made within the country are done by bike. The relatively short distances, little elevation changes, and special bike lanes give the country a biking culture. Unfortunately, this also results in thousands of biking accidents yearly. In 2019, there were 17,134 accidents involving a bike or e-bike. Out of these, 158 (0.92\%) were even fatal. In the following years, from 2019 to 2022, the number of fatalities only increased. To counter this, many studies have been conducted that focus on the causes and consequences of accidents. Factors such as seasonality, light conditions, and speed limit could all influence the outcome of a crash. Although these factors have been evaluated separately, more research is needed where multiple factors are analyzed together.
This project aims to fill this gap by proposing a solution combining association rules and visualization to allow for the analysis of the relationship between multiple factors and the different types of bicycle incidents, supporting analysts to geographically understand potential correlations of factors and incidents considering multiple levels of abstraction and detail.