Smart Marine Terminal -Technical Solution
We developed an AI-powered solution for a marine terminal in Romania, designed to enhance the efficiency of dockyard operations. This system processes live video feeds to analyze truck movements within the dockyard, identifying bottlenecks and optimizing the flow of vehicles to minimize delays and improve overall performance.
Key Technologies: CCTV, Python, PyTorch, Streamlit, PostGIS, SciPy, NetworkX
Real-time video feed analysis for truck movement tracking
Data mapping of dockyard layout, including parking areas, loading docks, and access roads
Traffic flow analysis and identification of congestion points
Predictive maintenance using data analytics
Integration of RFID/GPS technology for real-time truck location monitoring
Challenge
The dockyard faced inefficiencies due to congestion, delays in truck movements, and poor coordination between the dockyard staff and drivers. These operational bottlenecks led to higher operational costs and suboptimal use of available resources, negatively impacting port throughput and efficiency.
Solution
We developed a machine learning system that uses video feeds from CCTV cameras to track truck movements, analyze traffic flow, and identify congestion points in the dockyard. The system includes real-time data analytics to optimize truck scheduling and routing, and predictive maintenance features to preemptively address equipment failures. RFID and GPS technology were integrated to track truck locations, allowing for real-time management of truck statuses.
Results
The AI solution improved overall efficiency by streamlining the arrival and departure processes for trucks, reducing bottlenecks, and optimizing dockyard layout. It provided actionable insights that enabled the port to reduce delays, improve communication among stakeholders, and enhance decision-making through data-driven analytics. Continuous monitoring of traffic metrics and predictive maintenance further helped prevent equipment-related delays and optimize resource allocation.
Other projects you might be interested in:
Machine Learning for Data Pipeline Quality Assurance