LSTM Network Analysis of Vehicle-Type Fatalities on Great Britain's Roads
This work addresses traffic accident forecasting for road safety planning, but it appears incremental as it applies an existing method (LSTM) to new data without novel methodological claims.
This study tackled the problem of predicting road traffic accidents in Great Britain using LSTM networks, achieving results based on an extensive dataset from 1926 to 2022, but no concrete numbers were provided in the abstract.
This study harnesses the predictive capabilities of Long Short-Term Memory (LSTM) networks to analyse and predict road traffic accidents in Great Britain. It addresses the challenge of traffic accident forecasting, which is paramount for devising effective preventive measures. We utilised an extensive dataset encompassing reported collisions, casualties, and vehicles involvements from 1926 to 2022, provided by the Department for Transport (DfT). The data underwent stringent processing to rectify missing values and normalise features, ensuring robust LSTM network input.