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LSTM Network Analysis of Vehicle-Type Fatalities on Great Britain's Roads

arXiv 2312.08948 ↗First posted 14 Dec 2023cs.LG cs.AI cs.SEcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 2 of its 23 references that are in the map. First posted on arXiv 14 Dec 2023.

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References · 2

  1. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,428
  2. 2019Explaining a black-box using Deep Variational Information Bottleneck Approachcited by 35

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