Large-scale pavement roughness measurements with vehicle crowdsourced data using semi-supervised learning

Abstract

Rapid measurements of large-scale pavement roughness have long been a hot topic in pavement condition evaluation and maintenance. Most traditional methods rely on dedicated devices, such as laser, Lidar and so on, which should be set up on customized vehicles. With the rapid development of sensing technology, vehicles owned by the general public are empowered with the ability to collect vibration measurements themselves. This crowdsourced dataset is convenient, extensive coverage, inexpensive, and has high sampling frequency, making it a suitable source for large-scale pavement roughness evaluation. However, vehicle information is missing for these data due to privacy protection, which renders them quite difficult to directly use with traditional model-based methods. Thus, in this paper, we propose a semi-supervised learning (SSL) model to deal with the problem of incomplete data and multi-vehicle data fusion. A mathematical derivation of the ‘international roughness index’ (IRI) using in-car vibrations is established. Furthermore, given the multi-vehicle scenario, a self-training model is designed to iteratively estimate IRIs in a roadway network. Both the confidences of the vehicle parameters and IRI estimation are considered in the algorithm to improve its reliability and robustness. A full-car simulation model is constructed to verify the effectiveness of the proposed model. The results show that the overall relative error is less than 10% for 50 road sections in the network, which is a significant improvement compared to traditional multi-vehicle average models. The errors of the SSL model are found to be significantly dependent on the iteration order. Based on the proposed model, the coupled impact of the sampling rate and vehicle quantity on the model’s accuracy is further discussed. The proposed approach provides new insights into large-scale pavement roughness measurements.