TY - JOUR
T1 - ConvLSTM Neural Network based on Hexagonal Inputs for Spatio-Temporal Forecasting of Traffic Velocities
AU - Bahamondes, Francisco
AU - Peralta, Billy
AU - Nicolis, Orietta
AU - Bronfman, Andres
AU - Soto, Alvaro
N1 - Publisher Copyright:
© 2024 Copyright for this paper by its authors.
PY - 2024
Y1 - 2024
N2 - The spatial-temporal prediction of transit speeds is of great importance today as it allows for the anticipation and mitigation of vehicular congestion, thereby improving traffic efficiency. In machine learning, models such as ConvLSTM or Transformers enable reasonable predictions at the spatio-temporal level. However, these models typically assume a square grid configuration, which can limit the use of more convenient configurations in transportation, such as hexagonal grids. We propose a ConvLSTM neural network adapted to hexagonal grid sequences for transit speed prediction, incorporating a transformation of the hexagonal input to allow the use of standard spatial temporal architectures based on square grids. This work validates the proposed model through experiments comparing our approach with baseline methods using traffic data from freight transportation in the Metropolitan Region of Santiago, Chile. The results indicate that using hexagonal sequences improves the mean absolute error (MAE) in predicting freight traffic speeds by 2.7% compared to the base spatio-temporal ConvLSTM prediction model. For future work, we propose using larger databases and adapted transformers.
AB - The spatial-temporal prediction of transit speeds is of great importance today as it allows for the anticipation and mitigation of vehicular congestion, thereby improving traffic efficiency. In machine learning, models such as ConvLSTM or Transformers enable reasonable predictions at the spatio-temporal level. However, these models typically assume a square grid configuration, which can limit the use of more convenient configurations in transportation, such as hexagonal grids. We propose a ConvLSTM neural network adapted to hexagonal grid sequences for transit speed prediction, incorporating a transformation of the hexagonal input to allow the use of standard spatial temporal architectures based on square grids. This work validates the proposed model through experiments comparing our approach with baseline methods using traffic data from freight transportation in the Metropolitan Region of Santiago, Chile. The results indicate that using hexagonal sequences improves the mean absolute error (MAE) in predicting freight traffic speeds by 2.7% compared to the base spatio-temporal ConvLSTM prediction model. For future work, we propose using larger databases and adapted transformers.
KW - ConvLSTM
KW - Hexagonal inputs
KW - Spatio-temporal prediction
KW - Traffic velocities
UR - http://www.scopus.com/inward/record.url?scp=85210258966&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85210258966
SN - 1613-0073
VL - 3827
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
T2 - 3rd International Workshop on Spatio-Temporal Reasoning and Learning, STRL 2024
Y2 - 5 August 2024
ER -