Forecasting pavement performance with a feature fusion LSTM-BPNN model

Abstract

In modern pavement management systems, pavement roughness is an important indicator of pavement performance, and it reflects the smoothness of pavement surface. International Roughness Index (IRI) is the de-facto metric to quantitatively analyze the roughness of pavement surface. The pavement with high IRI not only reduces the lifetime of vehicles, but also raises the risk of car accidents. Accurate prediction of IRI becomes a key task for the pavement management system, and it helps the transportation department refurbish the pavement in time. However, existing models are proposed on top of small datasets, and have poor performance. Besides, they only consider cross-sectional features of the pavements without any time-series information. In order to better capture the latent relationship between the cross-sectional and time-series features, we propose a novel feature fusion LSTM-BPNN model. LSTM-BPNN first learns the cross-sectional and time-series features with two neural networks separately, then it fuses both features via an attention mechanism. Experimental results on a high-quality real-world dataset clearly demonstrate that the new model outperforms existing considerable alternatives.