Noida, where urban lifestyle meets natural beauty, is a vibrant city in Uttar Pradesh, India, that thrives with modern infrastructure and a booming business sector. As a key hub of growth and development, it is managed by the Greater Noida Industrial Development Authority (GNIDA). The city's complex road network is vital for its economic activities and urban mobility, making efficient and safe management a top priority for GNIDA, which led them to explore cutting-edge solutions for road infrastructure management.
Role of GNIDA
GNIDA, the primary authority responsible for the development and maintenance of road infrastructure in Greater Noida, sought to improve its road maintenance operations. The goal was to move away from traditional methods of road inspection, which were often labor-intensive and inefficient, and towards a more data-driven and proactive approach.
Challengees
GNIDA faced several significant challenges in managing its extensive road network:
- Inefficient Road Condition Monitoring: Traditional methods for road condition assessment were labor-intensive, time-consuming, and required costly equipment, often leading to delayed maintenance and higher costs.
- Fragmented Data Collection: Gathering comprehensive visual and location data across the road network was cumbersome, resulting in incomplete or inconsistent datasets.
- Inaccurate Distress Identification: Precise identification and classification of road conditions and distresses were critical for effective planning but challenging with manual inspections.
- Maintenance Planning: Without accurate and timely data, planning proactive maintenance schemes and prioritizing repairs were difficult, leading to reactive rather than proactive road management.
Solution
To address these challenges, RoadVision AI implemented a scalable and efficient solution that combined smartphone-based data collection with advanced AI-driven analysis. The mobile application allowed engineers to collect comprehensive visual and location data effortlessly, while the AI platform automated the identification and classification of road conditions and distresses. This approach provided GNIDA with objective, consistent assessments, and enabled proactive maintenance planning through predictive analytics. The integration of Digital Twin technology further enhanced GNIDA's ability to manage and maintain its road network effectively.
Data Collection Process
- Smartphone-Based Data Collection:some text
- User-Friendly Mobile Application: Engineers utilized a smartphone application to seamlessly collect visual and location data during road inspections.
- Efficient and Scalable: The mobile app enabled comprehensive data collection across the road network, ensuring extensive coverage with minimal effort.
- Survey Implementation:some text
- RoadVision AI Infrastructure: The survey was conducted using the RoadVision AI Data Collection App, along with a suction mount attached to the vehicle's windshield.
- Comprehensive Data Collection: This setup facilitated efficient and accurate capture of road condition data, essential for the project’s success.
Data Processing
- AI Intelligence Platform:some text
- Automated Data Processing: The AI platform automatically processed the collected data, identifying and classifying road types, conditions, and various distresses.
- Objective Condition Assessment: The platform ensured consistent and reliable assessments, enhancing the accuracy of road condition evaluations.
- Exportable Data Formats: Processed data was exported in formats like GeoJSON, allowing easy integration with GNIDA’s existing systems.
- Digital Twins and Predictive Analytics:some text
- Digital Twin Technology: Virtual replicas of the road network were created for in-depth analysis and strategic planning.
- Predictive Maintenance: Predictive analytics were used to forecast future road conditions and maintenance requirements, supporting proactive decision-making.
The objective of the Pilot
To demonstrate the solution’s effectiveness, RoadVision AI conducted a pilot on Greater Noida roads. The pilot involved:
- Scope of Pilot: The pilot focused on collecting and analyzing road condition data from a designated area in Greater Noida of about 5.24 KM.
- Key Activities:some text
- Engineers used the mobile application to collect visual and location data.
- The collected data was processed through the AI platform for condition assessment and distress classification.
- Comprehensive reports, including road chainage and inspection reports, were generated for analysis.
Key observations
- Area: Greater Noida, India
- Total Road Length: 5.24 KM (5240.00 m)
- Total Defect Count: 1879
- Total Defects Breakdown and Roads Status:some text
- Raveling: 1552
- Rut: 23
- Crack: 74
- Pothole: 65
- Shoving: 6
- Settlement: 159
Results
- The pilot conducted on 27 February 2024, effectively demonstrated the capabilities of RoadVision AI’s solution in real-world conditions.
- Automated data processing and objective condition assessments provided GNIDA with valuable insights into its road infrastructure.
- The pilot's success laid the foundation for the full-scale deployment of the solution across Greater Noida.
Benefits
- Increased Efficiency and Cost Savings:some text
- The smartphone-based data collection streamlined the inspection process, reducing reliance on expensive equipment and manual labor.
- Automated data processing and analysis provided quick and reliable results, enabling faster decision-making.
- Improved Road Condition Assessment:some text
- The AI platform’s objective assessments eliminated inconsistencies and biases associated with manual inspections.
- Comprehensive road condition reports facilitated better maintenance planning and resource allocation.
- Data Accessibility and Integration:some text
- All data was made available in formats compatible with GNIDA’s asset management systems, ensuring seamless integration.
- The web-GIS platform provided a centralized hub for accessing and visualizing road condition data, enhancing transparency and collaboration.
Outcome and Transformation
- Enhanced Maintenance Planning:some text
- Predictive analytics enabled GNIDA to transition from reactive to proactive maintenance, effectively extending the lifespan of road assets and reducing long-term costs.
- Accurate condition data allowed for prioritization of repairs, ensuring optimal use of resources and improving overall road management efficiency.
- Increased Efficiency and Cost Savings:some text
- The smartphone-based data collection streamlined inspections, reducing reliance on expensive equipment and manual labor, leading to significant cost savings.
- Automated data processing and analysis provided quick, reliable results, enabling faster and more informed decision-making.
- Improved Road Condition Assessment:some text
- The AI platform delivered objective and consistent assessments, eliminating the inconsistencies and biases often associated with manual inspections.
- Comprehensive road condition reports facilitated better planning, resource allocation, and informed decision-making for GNIDA.
- Data Accessibility and Integration:some text
- Processed data was made available in compatible formats, allowing for seamless integration with GNIDA's asset management systems.
- The web-GIS platform provided a centralized hub for accessing and visualizing road condition data, enhancing transparency, collaboration, and overall management.
- Successful Full-Scale Deployment:some text
- The full-scale deployment of RoadVision AI’s solution across Greater Noida established a robust system for continuous monitoring, assessment, and maintenance planning, ensuring safer and more sustainable roads.
- Foundation for Future Innovations:some text
- The partnership with RoadVision AI set a precedent for adopting innovative solutions in road infrastructure management and laid the groundwork for future advancements in AI models and digital twin technologies.
Conclusion
The successful implementation of RoadVision AI’s solution has revolutionized GNIDA’s road infrastructure management. With continuous monitoring and proactive maintenance strategies in place, GNIDA is well-equipped to maintain safer and more sustainable roads. RoadVision AI aims to continue supporting GNIDA in further enhancing its road management practices through advanced AI models and digital twin technologies.
