Time-Aware Gated Recurrent Unit Networks for Road Surface Friction Prediction Using Historical Data

TL;DR

This paper introduces a time-aware GRU-based model that effectively predicts road surface friction from incomplete historical data, enhancing safety by providing timely condition updates for transportation systems.

Contribution

It proposes a novel GRU-D model that handles missing data in road surface friction prediction, outperforming existing methods and analyzing the impact of missing data rates.

Findings

GRU-D outperforms baseline models in accuracy

Handling missing data improves prediction reliability

Analysis of missing rate effects on model performance

Abstract

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.