Transforming Urban Mobility with DigyCorp's Digital Twin and DCNexus Machine Learning
Client Overview
A major metropolitan transportation authority in a bustling urban area sought an innovative solution to streamline its multi-modal transport system, integrating autonomous ground vehicles, flying drones, and traditional transport modes. Faced with increasing demand, limited space, and the need to improve safety and efficiency, they turned to DigyCorp.
Challenge
The transportation authority needed a robust way to manage complex, simultaneous operations across multiple transportation modes in a high-density city. With autonomous ground vehicles and drones operating alongside public transit and traditional vehicles, they faced challenges in optimizing routes, minimizing congestion, and ensuring compliance with evolving safety regulations.
- Airspace Complexity: The complexity of controlling thousands of daily flights, exacerbated by environmental factors such as weather and flight variations.
- Limited Predictive Tools: Existing systems were not able to predict flight paths with the accuracy needed, leading to inefficiencies in air traffic control.
- Controller Workload: Human controllers handle large volumes of data in real time, often leading to fatigue and a high cognitive load, which can increase the risk of delays and operational issues.
These challenges necessitated a forward-looking solution that could assist air traffic controllers (ATCOs) in real-time decision-making while ensuring safety and efficiency.
The Solution
DigyCorp deployed a customized Digital Twin and DCNexus Machine Learning Platform tailored for urban multi-modal systems. The Digital Twin created a real-time, virtual replica of the entire urban transport ecosystem, integrating live data from autonomous vehicles, drones, and fixed infrastructure. DCNexus provided machine learning-driven insights to predict and optimize traffic flows, manage vehicle maintenance proactively, and adjust routes dynamically based on real-time conditions.
- Digital Twin: A high-fidelity, data-driven model of UK airspace that replicates real-world systems to support decision-making. This Digital Twin enables accurate simulation and testing of AI-driven air traffic control strategies.
- AI Agents: DigyCorp's technology supports the integration of AI agents designed to assist air traffic controllers by predicting potential aircraft conflicts and optimizing flight trajectories. The AI can handle both high-risk, tactical control and strategic airspace planning.
- Data-Driven Insights: The system leverages 10 million flight records from historical data, enhancing the AI’s ability to predict flight trajectories, respond to changes in weather conditions, and manage the complexities of UK airspace.
Results
With DigyCorp’s solution, the authority achieved significant operational gains:
- Increased Efficiency: Real-time simulations allowed for optimized routing of both ground and aerial vehicles, reducing congestion by 30% and cutting average travel times by 20%.
- Improved Safety & Compliance: Simulations ensured compliance with safety regulations, reducing incident rates by 25% and enhancing public trust in autonomous systems.
- Proactive Maintenance: Predictive maintenance for autonomous vehicles and drones decreased downtime by 40%, improving asset reliability and lifespan.
- Enhanced Environmental Impact: Optimized routes and reduced idle times resulted in a 15% decrease in emissions, aligning with the city’s sustainability goals.
Outcomes
- Increased Efficiency: The Digital Twin and AI system helped reduce the cognitive load on controllers by improving predictive capabilities, resulting in a 20% increase in operational efficiency.
- Reduced Delays: The integration of AI agents allowed for faster conflict detection and resolution, reducing delays by 15% during busy flight periods.
- Scalable Solution: The system enabled NATS to manage the increasing volume of flights more effectively, preparing for a future where air traffic control could handle 25% more flights.
- Enhanced Controller Support: AI provided real-time assistance to ATCOs, ensuring quicker decision-making and safer management of air traffic.
DigyCorp’s Digital Twin and DCNexus platform empowered the transportation authority to achieve seamless, multi-modal integration, transforming urban mobility into a safer, faster, and more sustainable system for millions of residents. The authority now looks to expand its partnership with DigyCorp to include further advancements in smart city infrastructure and mobility management.
Client Feedback
“DigyCorp’s Digital Twin platform revolutionized how we manage our diverse transportation modes. The predictive insights and real-time monitoring have been game-changers in keeping our city moving smoothly.”
Director of Urban Mobility Operations
Key Takeaways
- Enhanced Efficiency: Optimized traffic flow across autonomous and traditional transport modes reduced urban congestion by 35%.
- Improved Safety and Compliance: Real-time simulations helped ensure compliance with evolving safety standards, reducing incident rates by 30%.
- Proactive Asset Management: Predictive maintenance insights decreased downtime for autonomous vehicles and drones by 40%, improving fleet availability.
Key Metrics
Reduction in traffic congestion
Decrease in incidents related to autonomous and drone operations
Improvement in fleet availability due to predictive maintenance
faster travel times across key routes during peak hours
Ready to transform your city’s transportation network?
Contact DigyCorp to discover how our Digital Twin and DCNexus solutions can streamline multi-modal transport, improve safety, and enhance operational efficiency for urban areas.