Recurrent Neural Networks applied to Forecasting of Speed of Freight Transport in Dense Areas of Santiago, Chile

G. Diaz, D. Montecinos, O. Nicolis, B. Peralta

Resultado de la investigación: Contribución a los tipos de informe/libroContribución a la conferencia

Resumen

The speed prediction of freight transport is a growing and important task since in an ideal transport system the drivers could make optimal decisions about the route to follow. A computational method for predicting the the vehicle speeds is using autoregressive or other statistical models. Nonetheless, given the widespread success of modern neural networks, we believe that it could be feasible to have high prediction quality using this type of model. In this work, we propose the use of deep recurrent neural networks considering temporal multiple inputs for the speed prediction of freight vehicles. In particular, we use three models of neural networks for this task in specific dense areas of the city of Santiago of Chile. Standard metrics are reported to measure the models quality. Experiments showed that one of the proposed models using the information from the previous seven days give better results than other two proposed models. From the results, we conclude that these models have a good predictive capacity being able to predict the speed in different points on the Santiago map. As future work, we hope to test our models over a larger area, in addition to the incorporation of new variables that can influence and improve our models such as economic variables or relevant dates with high expected traffic.

Idioma originalInglés
Título de la publicación alojadaIEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781728131856
DOI
EstadoPublicada - nov 2019
Evento2019 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019 - Valparaiso, Chile
Duración: 13 nov 201927 nov 2019

Serie de la publicación

NombreIEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019

Conferencia

Conferencia2019 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019
PaísChile
CiudadValparaiso
Período13/11/1927/11/19

    Huella digital

Áreas temáticas de ASJC Scopus

  • Inteligencia artificial
  • Ingeniería eléctrica y electrónica
  • Control y optimización
  • Redes de ordenadores y comunicaciones
  • Hardware y arquitectura
  • Sistemas de información
  • Gestión y sistemas de información
  • Ingeniería energética y tecnologías de la energía

Citar esto

Diaz, G., Montecinos, D., Nicolis, O., & Peralta, B. (2019). Recurrent Neural Networks applied to Forecasting of Speed of Freight Transport in Dense Areas of Santiago, Chile. En IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019 [8988101] (IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CHILECON47746.2019.8988101