With the rapid increase of roads constructed in the city, pavement performance prediction becomes a crucial problem for the city's authorities. Traditional approaches for pavement management rely on the expert's knowledge, which is not only inaccurate but also labor-consuming. With the development of information technologies, especially the accumulation of big data in the smart city, now it is a great opportunity to leverage big data collected from multiple domains in the city to predict the pavement's performance. Thus, this paper proposes a novel framework for the pavement performance based on the analysis of big data in the smart city. First, we extract a number of features from the data collected, which is relevant to the pavement performance. Then, since some features may be incomplete, we use the tensor decomposition based approach to find the implicit correlations between features, predict the performance. Finally, we analyze, discuss the effectiveness of our framework together with its possible applications.
['Lixia Bao', 'Kai Yang', 'YiBo Wang', 'Junfeng Zhao']
A Novel Pavement Performance Prediction Framework in Smart City Based on Tensor Decomposition