Recurrent Neural Networks for Pavement Performance Forecasting: Review and Model Performance Comparison

Abstract

Accurate pavement performance forecasting is critical in supporting transportation agencies’ predictive maintenance strategies: programs that prolong pavement service life while using fewer resources. However, because of the complex nature of pavement deterioration, high accuracy for long-term and project-level pavement performance forecasting is challenging to traditional models. Therefore, researchers have taken advantage of machine learning (ML) technology to create more sophisticated models in recent years. However, there are no extant studies that compare different ML models on a singular, real-world, large-scale, and comprehensive pavement data set to evaluate their capability for pavement performance forecasting. Thus, the goal of this study is to critically evaluate ML models, such as multiple linear regression (MLR), fully connected neural network (FCNN), recurrent neural network (RNN), gated recurrent unit (GRU), long short-term memory (LSTM), and a hybrid LSTM-FCNN model, on Florida’s statewide, 31 year historical pavement data set. The results demonstrate that the RNN, GRU, LSTM, and LSTM-FCNN models perform significantly better than MLR and FCNN for predicting time-series pavement condition, with the LSTM-FCNN model performing the best. This result provides a valuable demonstration and recommendation to transportation agencies and researchers that RNN-based ML models are a promising direction to improve the accuracy of pavement performance forecasting.

Predicting Concrete Pavement Condition for Sustainable Management: Unveiling the Development of Distresses through Machine Learning

“…Additionally, the necessity of including machine learning approaches for deriving more precise conclusions from extensive road pavement networks has been increasingly recognized [22]. A notable example comes from the Florida Department of Transportation (DOT), where they have introduced methods involving recurrent neural networks (RNNs), Deep Neural Networks (DNNs), gated recurrent units (GRUs), long short-term memory (LSTM), and hybrid (LSTM-FCNN) models to process time series data representing continuous pavement conditions [23]. Deep Neural Networks (DNNs) have been utilized as a predictive tool for the pavement condition index by using a dataset of 536,848 samples.…”

Predicting Traffic Flow Parameters for Sustainable Highway Management: An Attention-Based EMD–BiLSTM Approach

“…As one of the deep learning algorithms, the recurrent neural network (RNN) can aptly capture the spatiotemporal evolution of traffic flow. However, due to issues like gradient explosion and gradient vanishing, it cannot grasp the long-term spatiotemporal evolution patterns [4]. Long short-term memory (LSTM) networks introduced the concept of gates based on RNNs, including forget gates, input gates, and output gates.…”

“…Within the traffic flow prediction methods based on statistical learning, time series methods predict the future based on the development patterns of historical traffic flow data. (2) With the global rise in artificial intelligence and big data technologies, scholars from various countries have also started using artificial intelligence techniques for traffic flow parameter prediction, primarily encompassing the recurrent neural network (RNN) [4], the convolutional neural network (CNN), the graph neural network (GNN) [5] and others.…”

Ensemble Learning Approach for Developing Performance Models of Flexible Pavement

“…Many of the recently developed models for pavement performance are based on empirical in-service data [28][29][30][31][32]. Based on the reviews of prior studies as presented above, it was observed that there is a need to include more pertinent data from the construction phase of the pavement with the in-service data when modeling the pavement performance.…”