Road Maintenance Planning Using ML and Sensor Data
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Solution Overview
Problem
Municipalities face challenges in efficiently planning and executing road maintenance due to high costs and labor-intensive manual processes, which are often unsustainable for smaller budgets, and road signage is prone to weather-related damage and vandalism.
Innovation Solution
A system utilizing integrated cameras and machine-learning algorithms on vehicles to capture and analyze images of road conditions, combined with accelerometer data and GPS readings, to identify distresses and predict maintenance needs, providing a web application for municipalities to model maintenance strategies and optimize schedules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection methods are used with human inspectors and dedicated vehicles, then road condition assessment can be performed, but maintenance costs and operational complexity increase significantly
Solution Approach 1:
The patent replaces manual mechanical inspection processes with an automated optical and sensor-based system. Cameras mounted on vehicles capture images of road surfaces, and machine learning algorithms automatically analyze these images to detect distresses such as cracks and potholes. This substitution eliminates the need for dedicated inspection vehicles and human inspectors, thereby reducing device complexity and operational costs while maintaining or improving measurement precision.
Solution Approach 2:
The system enables self-service through autonomous operation. The cameras and sensors automatically capture data, the machine learning models independently analyze the captured images to identify road conditions, and the system generates maintenance recommendations without human intervention. This self-service capability reduces the need for specialized inspection equipment and personnel, simplifying the overall maintenance system.
2Ease of operation
If manual inspection and rating systems are used to select assets for repair, then maintenance decisions can be made, but time consumption and labor requirements increase
Solution Approach 1:
The patent replaces manual decision-making processes with automated algorithmic analysis. Machine learning models process captured images and automatically generate priority rankings for maintenance assets based on detected distresses. This substitution eliminates the need for human inspectors to manually rate and prioritize assets, dramatically reducing time consumption while improving consistency and objectivity in maintenance decision-making.
Solution Approach 2:
The system implements continuous feedback loops where captured road condition data is immediately analyzed by machine learning models, and maintenance priority recommendations are generated in real-time. This feedback mechanism enables rapid, data-driven decision-making without manual intervention, reducing the time required for asset selection and repair planning while improving operational ease.
3Reliability
If dedicated maintenance vehicles and personnel are deployed for road inspection, then comprehensive road monitoring is achieved, but operational costs and resource requirements increase
Solution Approach 1:
The patent makes inspection vehicles universal by equipping them with cameras and sensors that can monitor multiple road assets simultaneously. The same vehicle that captures road surface images can also detect signage conditions, pavement markings, and other infrastructure elements. This multi-functionality eliminates the need for dedicated inspection vehicles and personnel for each asset type, reducing resource consumption while maintaining comprehensive monitoring reliability.
Solution Approach 2:
The system merges multiple inspection functions into a single integrated platform. Cameras, sensors, and machine learning analysis are combined to simultaneously assess road surfaces, signage, and other infrastructure. This consolidation reduces the number of dedicated vehicles and personnel required, decreasing energy and resource consumption while maintaining or improving monitoring reliability through comprehensive data collection.
Data Source
AI summary
The present invention is a system and method for delivering optimized road maintenance analysis to a municipality. The instant innovation scans roadways for distressed street surfaces, damaged signage, and other less-than-optimal municipal assets. Data is collected by multiple municipal fleet vehicles as such vehicles drive upon roads within a municipality. Collected data are analyzed by a machine learning algorithm using criteria that most directly correspond to multi-year road quality predictions. The instant innovation provides to a user one or more suggestions and scenario results for roadway maintenance strategies based upon the data analysis.