# A Review of Long-Term Skid Resistance of Asphalt Pavement
## 6 Outlook  
In recent years, a series of research studies has been conducted at home and abroad on the L-TSR performance of AP, and certain research results have been achieved. However, domestic and foreign research on the L-TSR of AP has not been comprehensive, and subsequent research can be carried out from the following shortcomings of the in-depth research and discussion:  
(1) With the rapid development of sensor technology, artificial intelligence, and big data analysis, the future testing of the skid resistance of outdoor asphalt pavements will move towards intelligence and automation. Intelligent sensors will be able to collect various parameters of the pavement in real time, including texture, humidity, temperature, and mechanical data during vehicle driving, and automatically transmit them to the back end for analysis and processing. The data analysis model based on artificial intelligence algorithms can quickly and accurately evaluate the skid resistance of the pavement, not only reducing human errors but also enabling real-time monitoring and early warning of complex road conditions. (2) In the future, indoor accelerated loading equipment will be closely integrated with virtual models. By creating high-precision virtual models of asphalt pavements, different loading schemes and environmental conditions can be pre-simulated in a virtual environment to predict the changing trends of pavement performance, providing guidance for actual tests. Meanwhile, the data obtained from actual tests will be fed back into the virtual model to modify and optimize it, achieving mutual promotion between virtual simulation and actual testing, improving research efficiency and reducing testing costs. (3) In the future, the analysis of the skid resistance mechanism of AP should include multi-scale texture analysis, considering the influence of seasonal and long-term changes, exploring the application of new materials and technologies, studying the stability of base layer materials, and deeply understanding the influence of terrain on skid resistance. At the same time, the skid resistance of AP is quantitatively evaluated, and the results are compared with the macro-texture, microstructure, and apparent morphology to fully verify the skid resistance of AP. (4) When exploring the decay law of the long-term skid resistance of asphalt pavements, a comprehensive consideration of various complex factors will be carried out. From the micro level, such as the abrasion of aggregates and the aging of asphalt, to the macro level, including the cumulative annual traffic loads and the periodic cycles of seasons and climate, the gradual change trajectory of skid resistance under the individual and synergistic effects of each factor will be accurately sorted out. On the other hand, relying on big data and the Internet of Things, vast amounts of multi-source and dynamic pavement information will be widely collected. By using advanced algorithms such as deep learning and reinforcement learning, the hidden complex relationships behind the data will be deeply explored to build a highly intelligent decay model of skid resistance. (5) The combined prediction model of the skid resistance of AP will make up for the deficiency of a single model to a certain extent and improve the prediction accuracy. In the future, we should comprehensively consider various influencing factors, use advanced calculation methods and algorithms, adopt probability and random models, make full use of experimental and field test data, and develop multivariate and high-precision combined prediction models. Validation, Conceptualization, Writing--original draft, Writing--review & editing, Data curation, Y.C. ; Data curation, Z.L. ; Methodology, Y.W. ; Supervision, G.L. ; Funding acquisition, Investigation, Project administration, Resources, X.Y. All authors have read and agreed to the published version of the manuscript. Supported by the Guangxi Science and Technology Program (Grant No. GuikeAB24010355).