Road Deterioration Prediction System Using Dynamic Mapping
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Solution Overview
Problem
Current road maintenance systems lack the ability to efficiently determine the area and time for repairs due to varying road deterioration caused by traffic conditions and weather, necessitating a more dynamic and predictive approach.
Innovation Solution
A deterioration prediction system that uses road inspection data and prediction time points to forecast the deterioration level of roads, displaying this information on a map in a manner corresponding to the predicted deterioration level, allowing for informed repair planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed repair cycle is determined for each road, then inspection and repair plans can be formulated systematically, but the varying deterioration rates due to traffic conditions and weather cannot be accounted for
Solution Approach 1:
The system transitions from a static fixed repair cycle approach to a dynamic predictive approach. It uses machine learning models that continuously learn from inspection data, traffic conditions, and weather patterns to adaptively predict deterioration rates. The repair cycle becomes flexible and tailored to each road section's actual deterioration trajectory rather than following a uniform schedule.
Solution Approach 2:
The system performs preliminary prediction of future deterioration states before actual deterioration occurs. By analyzing historical inspection data and environmental factors, it forecasts when and where roads will reach critical deterioration thresholds, enabling proactive repair planning ahead of time rather than reacting to actual deterioration.
2Measurement precision
If detailed inspection data is collected for each road section, then accurate deterioration prediction can be achieved, but the complexity of data management and analysis increases
Solution Approach 1:
The system creates simplified digital representations (copies) of road conditions through standardized inspection data collection. Instead of managing complex raw data from multiple sensors and inspection methods, it converts all inputs into a unified deterioration index that can be processed by machine learning models, maintaining measurement precision while reducing data management complexity.
Solution Approach 2:
The system transforms diverse inspection data (visual assessments, crack measurements, roughness indicators) into standardized parameters and features that feed into the prediction model. By changing the form of data into consistent numerical parameters, it simplifies analysis while preserving the essential information needed for accurate deterioration prediction.
3Reliability
If repair plans are made according to actual deterioration state of each road, then maintenance effectiveness is improved, but the difficulty of determining area and time for repairs increases
Solution Approach 1:
The system implements continuous feedback loops where inspection data from the field feeds into the prediction model, which generates repair recommendations. These recommendations are then implemented and their effectiveness is measured by subsequent inspections, creating a closed-loop system that continuously improves prediction accuracy and repair timing while simplifying the decision-making process for determining repair area and time.
Solution Approach 2:
The system replaces manual expert judgment and complex analytical processes with automated machine learning models. The AI algorithms automatically analyze inspection data, predict deterioration trajectories, and generate repair recommendations, eliminating the need for manual assessment of when and where repairs are needed while improving consistency and reliability.
Data Source
AI summary
A deterioration prediction system according to an aspect of the present disclosure includes: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: predict a deterioration level of the road at prediction time points according to road inspection data and the prediction time points, the road inspection data obtained by inspecting a road and the prediction time points indicating future time points, and superimpose the deterioration level on a map for each of the prediction time points in a display mode according to the deterioration level.