🗺️ Software concept for predicting and mitigating future potholes in roadways.

This concept aims to use data analysis and predictive modeling to anticipate potholes, particularly in different scenarios, and provide valuable insights for maintenance and infrastructure planning. Predicting potholes is a challenging task due to the multitude of interconnected factors involved, the dynamic nature of the environment, and the complexity of data analysis required. While advancements in technology and data analysis may improve predictive capabilities, pothole prediction remains a complex problem with inherent uncertainties.

1. Site Locations

• Determine the roads to use for predicting future potholes.

2. Historical Pothole Data

• Collect pictures of roads.
• Conduct studies of road surfaces.
• Document weather conditions.
• Record location data.
• Measure the traffic.
• Perform site analysis.

3. Predictive Scenario Indicators

• Identify factors that could influence future potholes, including: heavy traffic, bad weather conditions, road surfaces.

• Types of roads: dirt roads, high traffic roads, old roads.

4. Model Selection for Pothole Prediction

• Choose an appropriate forecasting model.

5. Scenario Analysis

• Develop different scenarios.

6. Probability Tolerance and Fault Analysis for Potholes

• Calculate Probability Tolerance using the formula: (Measured Value - Expected Value) / Expected Value.

• Use Probability Tolerance to determine allowed faults.

7. Scenario Analysis for Roadways and Potholes

• Identify places with the highest number of faults or predicted pothole-prone areas.

8. Interpretation and Reporting for Pothole Predictions

• Analyze the results obtained from the model.

• Prepare a comprehensive Pothole Prediction Report.

Pothole Detection

CityROVER

ℹ️ This software is free and open-source; anyone can redistribute it and/or modify it.