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Roads as vital public assets are the backbone for transportation systems and support constant societal development. Recently, data-driven technologies such as digital twins and especially machine learning have shown great potential to maintain the service level of the existing road infrastructure by accurate future condition modelling and optimal maintenance treatment recommendations. However, the pavement community suffers from inadequate data and errors experienced in data collection, which unavoidably limits machine learning performance. In addition, focusing solely on data without considering the underlying physical behaviour remains as a challenge for the practical implementation of machine learning. To this end, this study provides a machine learning based approach to predict road rutting taking into account the machine learning uncertainties. The US Long-Term Pavement Performance public database has been used as the main data source while supplementary synthetic data was added using Finite Element simulations based on physics. The obtained results indicate that adding extra simulation data improved the model’s short-term prediction accuracy by 4.4% and reduced the long-term prediction uncertainty by 6.76%. The approach could potentially mitigate the issue of lack of data and the uncertainties around the data collected, by integrating existing understanding of pavement physical behaviour into the machine learning modelling pipeline.

In view of the complex road conditions in today's cities, the traditional prediction methods for road conditions are not so accurate, and the optimization algorithm for the logistics distribution path is not sensitive to changes in the road conditions so that its application in an actual logistics distribution system is not effective. This article proposes a road condition prediction and logistics distribution path optimization algorithm based on traffic big data. First, it analyses the characteristics of the road condition information of traffic big data. By combining the powerful feature extraction and self-learning ability of a deep belief network, it establishes a road condition prediction model based on a deep belief network and completes the model training and verification through the learning of traffic big data. Then, it combines the road condition prediction (result) information, traffic network information, and logistics distribution information to construct the time-share weighted traffic network. It then modifies the access set and pheromone variables of the ant algorithm based on the time-share traffic network to establish the road condition prediction and logistics distribution path optimization algorithm based on traffic big data. Finally, it conducts comparative experiments with other logistics distribution path optimization algorithms. The experimental results show that the proposed algorithm is superior to other logistics distribution optimization algorithms. Therefore, this algorithm is an effective method for optimizing logistics distribution. [ABSTRACT FROM AUTHOR]