The detection of various cracks on pavement surfaces has drawn more and more attention from pavement maintenance engineers. In the traditional pavement image segmentation, due to the small area of the pavement cracks, the gray level of crack pixels only accounts for a very small portion in the grayscale histogram, making it difficult to segment. This paper developed an improved Otsu method integrated with edge detection and a decision tree classifier for cracking identification in asphalt pavements. An image preprocessing approach including Gaussian function-based spatial filtering and top-hat transform is firstly proposed to reduce the influence of poor shading and lighting effects significantly. Four edge detection operators including Prewitt, Sobel, Gauss–Laplace (LoG), and Canny are evaluated. The Canny edge detection has demonstrated outstanding performance in crack detection; this algorithm helps to obtain more details of both cracks and noises. The Sobel and LoG operators show similar image segmentation and retain fewer noises. The decision tree classifier based on the ID3 algorithm can effectively classify different types of cracks including transverse, longitudinal, and block ones.

In order to realize the prediction of freeway travel time, a short-term travel time prediction model based on LightGBM (Light Gradient Boosting Machine) is proposed under the influence of weather factors, time period factors, and traffic factors. These factors are called as the features used for increase prediction accuracy. The travel time of a single vehicle is determined by license plate recognition data of two adjacent video monitors in Shaoxing section of Shanghai-Hangzhou-Ningbo Freeway, and a better travel time data set is constructed by data preprocessing. The feature data are determined by Pearson correlation. Based on the analysis of main optimization parameters in LightGBM, the short-term average travel time is predicted, the MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error) obtained by experiments are satisfying, indicating that LightGBM model has high accuracy and good fit. Finally, through comparison with KNN (K-Nearest Neighbor) model and GBDT (Gradient Boosting Decision Tree) model, the prediction accuracy and training speed both show that LightGBM has good advantages in predicting short-term freeway travel time.

No abstract

No abstract

No abstract