1 research outputs found
Time-Aware Gated Recurrent Unit Networks for Road Surface Friction Prediction Using Historical Data
An accurate road surface friction prediction algorithm can enable intelligent
transportation systems to share timely road surface condition to the public for
increasing the safety of the road users. Previously, scholars developed
multiple prediction models for forecasting road surface conditions using
historical data. However, road surface condition data cannot be perfectly
collected at every timestamp, e.g. the data collected by on-vehicle sensors may
be influenced when vehicles cannot travel due to economic cost issue or weather
issues. Such resulted missing values in the collected data can damage the
effectiveness and accuracy of the existing prediction methods since they are
assumed to have the input data with a fixed temporal resolution. This study
proposed a road surface friction prediction model employing a Gated Recurrent
Unit network-based decay mechanism (GRU-D) to handle the missing values. The
evaluation results present that the proposed GRU-D networks outperform all
baseline models. The impact of missing rate on predictive accuracy, learning
efficiency and learned decay rate are analyzed as well. The findings can help
improve the prediction accuracy and efficiency of forecasting road surface
friction using historical data sets with missing values, therefore mitigating
the impact of wet or icy road conditions on traffic safety