# Pavement Distress Initiation Prediction by Time-Lag Analysis and Logistic Regression
## 1 Introduction
With the development of image processing technology in pavement surface distress detection, especially the accuracy of pavement surface distress detection and segmentation based on convolutional neural networks, detection has been significantly improved, and the automatic detection technology of pavement surface distress based on images has been gradually accepted and applied to pavement engineering [15; 16]. Automatic detection of pavement surface distress usually loads a vehicle with a front-view [17; 18] or rear-view [19; 20] camera to capture images of the pavement surface. Convolutional neural network-based distress detection usually requires thousands of images to train the model [21], and image data can be obtained from open source datasets [22] or self-collected data [23]. Subsequently, the pixel ranges with pavement distress are extracted from the original images by well-established image recognition frameworks, such as YOLO [24], RCNN [25], and transfer learning [26]. YOLO and RCNN frameworks require larger single-scene datasets for training, and the accuracy for pavement distress detection in a single scene is higher, and the transfer learning framework requires more multi-scene datasets for training, so the robustness for pavement distress detection in multiple scenes is better. Finally, the detection of high-frequency continuous pavement distress occurrence and deterioration can be achieved due to the automation and low cost of pavement distress detection. [PERSON] et al. [27] matched the pavement distress data collected on multiple consecutive days by constructing a spatio-temporal correlation algorithm to eliminate duplicate pavement diseases, thus achieving high-frequency continuous tracking of individual distress occurrence and deterioration. However, the abovementioned pavement distress detection techniques can only provide formatted information such as the classification attributes and appearance of geometric features of the pavement distress, and there is less research on how to analyze the characteristics of pavement distress' short-period initiation and deterioration from these formatted data and construct a pavement distress short-period prediction model. Similar to studies in environmental science [28] and geophysics [29] on the influence of physical systems by environmental factors, road pavements, despite their exposure to the natural environment, are not affected by the action of environmental factors in a simultaneous manner. [PERSON] et al. [30] used a long- and short-term memory (LSTM) neural network to develop a pavement friction prediction model based on daily pavement performance data collected over a long period of time, considering the time-series characteristics of pavement conditions, and found that the prediction accuracy was affected by the time lag and the prediction time interval. [PERSON] et al. [31] used Dutch traffic data and weather data to construct count data models with temporal interdependence based on integer autoregressive models and found that daily changes in weather conditions had a significant effect on the number of crashes caused by changes in road conditions. However, fewer studies have focused on quantitatively studying the relationship between pavement distress generation and environmental factors. In the microstructure of pavement asphalt layers, [PERSON] et al. [32] and [PERSON] et al. [33] found that the movement of moisture in the asphalt mixture due to vehicle loading under the influence of ambient temperature and humidity will reduce the strength and service life of the asphalt mixture. However, at the macroscopic level, most prediction models consider environmental factors as independent variables due to the lack of continuous decay detection data of pavement distress under natural conditions, and less consider the time series effect of environmental changes under short-period conditions, so it is meaningful for short-period pavement distress occurrence and deterioration prediction models to consider environmental factors.