| Popis: |
This paper presents a study on road accident data in Senegal by identifying associations within accident cases. We use unsupervised classification techniques such as clustering and association rule mining. Pre-processing of our data set revealed subgroup structures within the data. To reduce the association rule mining algorithm's search space, we used the k-modes clustering method as the main segmentation task on road accident data. Then, association rule mining helps identify the different circumstances associated with an accident in each group obtained by the k-modes algorithm. The results of the study show that to improve road safety, the authorities in charge of road transportation could orient their prevention policies towards three main aspects: vehicle fleet renewal, respect for the Highway Code, and prohibition of large vehicles circulation, such as trucks used for goods transportation at rush hours. |
