Predicting Motor Vehicle Theft in Santiago de Chile using Graph-Convolutional LSTM

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Resumen

Vehicle theft represents one of the most frequent crimes in Chile and in the world. In this work, we propose an application of the GCLSTM (Graph-Convolutional Long Short Term Memory) neural network that combines a graph convolutional model with a LSTM for the prediction of vehicle thefts in the metropolitan region of Chile the graph architecture considers the characteristics found in the neighbors to an area, assuming that the thefts of vehicles in nearby municipalities have similar patterns. For implementing the GCLSTM, first a smoothing technique based on LOESS regression was used for denoising the number of theft events for day, then the smoothed series of the last 30 days was considered as the input of the GCLSTM neural network for predicting the number of thefts in the following day the results provided a better performance of the GCLSTM compared to a traditional LSTM, achieving an R2 of 0.86.

Idioma originalInglés
Título de la publicación alojada2020 39th International Conference of the Chilean Computer Science Society, SCCC 2020
EditorialIEEE Computer Society
ISBN (versión digital)9781728183282
DOI
EstadoPublicada - 16 nov 2020
Evento39th International Conference of the Chilean Computer Science Society, SCCC 2020 - Coquimbo, Chile
Duración: 16 nov 202020 nov 2020

Serie de la publicación

NombreProceedings - International Conference of the Chilean Computer Science Society, SCCC
Volumen2020-November
ISSN (versión impresa)1522-4902

Conferencia

Conferencia39th International Conference of the Chilean Computer Science Society, SCCC 2020
País/TerritorioChile
CiudadCoquimbo
Período16/11/2020/11/20

Áreas temáticas de ASJC Scopus

  • Ingeniería (todo)
  • Informática (todo)

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