SYSTEM AND METHOD FOR PREDICTING ROAD CRASH RISK AND SEVERITY USING MACHINE LEARNING TRAINED ON AUGMENTED DATASETS

摘要

The present invention relates to systems and methods for synthesising data based on an input dataset in order to generate an augmented dataset for road risk modelling and prediction using machine learning. Particularly, the invention relates to methods of accident data pre-processing and road accident risk modelling to predict road accident frequency and severity using contextual, environmental and road information (and machine learning and deep learning techniques) using synthesised data to enhance an existing dataset in order to create training data for the modelling. Aspects and/or embodiments seek to provide a method for enriching datasets for use as training data, for example to use in predicting road crash risk and severity using historical positive data for crashes and enriched contextual data for roads. Further aspects and/or embodiments seek to provide methods for training models using this training data and using these trained models for inference of classifications of road crash risk and severity.