Data-driven decision-making model : for Road Maintenance Prediction

Sammanfattning

Industry 4.0, as well as the increasing use of artificial intelligence and machine learning, have made it possible to analyse large amounts of data and improve performance across many businesses and sectors. These sectors have significantly increased their reliance on data when making decisions. This thesis examines the influence of data-driven decision-making on road maintenance planning in Sweden. I gathered data related to road state in accordance with the Swedish Road Maintenance Standard, focussing on the International Roughness Index (IRI) and rut depth as primary factors. This data analysis enabled the identification of maintenance needs within three separate time frames: immediate, the next five years, and long-term. Overall, the model predicted maintenance needs based on the International Roughness Index (IRI) with up to 96% accuracy. However, the model's accuracy dropped to only 67% when predicting maintenance needs over the next five years. In contrast, the model that predicted maintenance needs based on rut depth demonstrated high accuracy across all three-time frames, achieving up to 92% accuracy.The model demonstrated that modern road condition variable data are crucial to prediction. In terms of predictions, 2023 IRI measurements were the most important. Based on our findings, this thesis improves data-driven decision-making in Swedish road maintenance, resulting in more effective resource allocation and a decrease in emergency maintenance expenses. Moreover, the study highlights the value of collecting and utilising more accurate and thorough road state data to enhance these models.