Towards Data-driven Decision Making for Predictive Road Maintenance: Leveraging Artificial Intelligence and Machine Learning to improve the Process of Road Maintenance at the City of Copenhagen

Abstract

Using data to optimize processes and enable data-driven decision making to increase productivity is on today’s agenda of many organizations. In the context of road maintenance, data-driven technologies can have a huge impact by enhancing processes, reducing costs and increasing road safety. This thesis investigates how data-driven technologies can improve the process of road maintenance at the City of Copenhagen as case study. Three currently used stand-alone systems have been combined to generate new data insights. This thesis proposed a new approach to develop a Pavement Condition Index (PCI) to objectively compare conditions across different roads based on an existing lifetime calculation. A linear regression and a XGBoost model have been applied to predict the respective index and to identify the relevant features causing road degradation. The best Root Mean Squared Error (RMSE) of 11.74 on the test data is achieved by an optimized XGBoost model with an adjusted R2 score of 0.9011. The model identified large cracks, alligator cracks and rutting as the three most influential features. Moreover, an XGBoost model was trained to predict potholes based on historical data. The received scores show a RMSE improvement by 30% compared to the baseline solutions when including all available data. However, with an adjusted R2 score of 0.3977 for the best model, the results proof the theoretical potential but do not fulfill the maturity degree to put the model into production. In a theoretical part the thesis comes up with a proposal for future road maintenance scenarios focusing on automating data collection and damage classification. Based on the findings, the requirements to use road maintenance data for future data-driven decision making are formulated in five recommendations for the City of Copenhagen. In summary, the current state of data maturity in our case study needs to improve to effectively leverage data-driven technologies for decision-making.