Pavement condition monitoring is an important task in road asset management and efficient abnormal pavement condition detection is critical for timely conservation management decisions. The present work introduces a mobile pavement condition monitoring approach utilizing low-cost sensor technology and machine-learning-based methodologies. Specifically, an on-board unit (OBU) embedded with an inertial measurement unit (IMU) and global positioning system (GPS) is applied to collect vehicle posture data in real time. Through a comprehensive analysis of both time domain and frequency domain data features for both normal and abnormal pavement conditions, feature engineering is conducted to identify how the most important features affect abnormal pavement condition recognition. Six machine learning models are then developed to identify different types of pavement conditions. The performance of different algorithms and the significance of different features are then analyzed. Moreover, the influence of vehicle speed on pavement condition assessment is further examined and classification models for different speed intervals are developed. The results indicate that the random forest (RF) model that considers vehicle speed achieves the best performance in pavement condition monitoring. The outcomes of the present work would contribute to cost-effective pavement condition monitoring and provide an important reference for pavement maintenance sectors.

Traffic safety on expressway has received increasing attention, and vehicle lateral position deviation is one of the key risk factors causing expressway traffic accidents. Exploring the influencing mechanism of different factors on vehicles’ lateral position stability is therefore important for ensuring traffic safety and refined management of expressway. Taking Guangzhou-Shenzhen Expressway as an example, vehicles’ lateral position and driving characteristics passing by a specific location were collected. Based on the observation data, a comprehensive analysis of vehicles’ moving characteristics and the distribution of vehicles’ lateral position were conducted. Then a quantile regression (QR) model is built to further explore the potential influence of various factors on vehicles’ lateral position shifts. Based on the results, there is a higher possibility for left shift of vehicle lateral positions and vehicle speed has a significant negative effect on the lateral position shift of small-sized and medium-sized vehicles. Suggestions for refined traffic management and control to enhance safety driving on expressway are then proposed.