2022/05/20 by Paschalis Lagias, George D. Magoulas, Lagias, Paschalis +5
Engineering · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #I.2.6 #Machine Learning (cs.LG) #Traffic Prediction and Management Techniques #Traffic and Road Safety
paper · doi:10.48550/arxiv.2205.10441
openalex publication_date 2022/05/20 · openalex created_date 2022/05/26 · openalex updated_date 2026/07/28
The paper introduces a new dataset to assess the performance of machine learning algorithms in the prediction of the seriousness of injury in a traffic accident. The dataset is created by aggregating publicly available datasets from the UK Department for Transport, which are drastically imbalanced with missing attributes sometimes approaching 50% of the overall data dimensionality. The paper presents the data analysis pipeline starting from the publicly available data of road traffic accidents and ending with predictors of possible injuries and their degree of severity. It addresses the huge incompleteness of public data with a MissForest model. The paper also introduces two baseline approaches to create injury predictors: a supervised artificial neural network and a reinforcement learning model. The dataset can potentially stimulate diverse aspects of machine learning research on imbalanced datasets and the two approaches can be used as baseline references when researchers test more advanced learning algorithms in this area.