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

Abiodun Finbarrs Oketunji, James Hanify, Salter Heffron-Smith
12/14/2023
cs.LGcs.AIcs.SE

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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.

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Cite this paper

@article{oketunji2023lstm,
  title  = {LSTM Network Analysis of Vehicle-Type Fatalities on Great Britain's Roads},
  author = {Abiodun Finbarrs Oketunji and James Hanify and Salter Heffron-Smith},
  year   = {2023},
  eprint = {2312.08948},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2312.08948v2}
}

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