Ulusal Projeler

The project aims to enhance the METIS software, originally developed for dynamic intersection management, to elevate Turkey's Traffic Monitoring Centers to advanced Traffic Control Centers. METIS will be transformed into a comprehensive traffic management platform, aligning with future investments planned by Metropolitan Municipalities.

The project aims to develop integrated software solutions for optimizing traffic management and ensuring more reliable journeys. Using big data and AI, the software will analyze traffic flow, detect congestion, and provide forward-looking forecasts to enhance the efficiency and reliability of transportation systems.

The project aims to develop a Public Transport Optimization Platform to enhance the efficiency of public transport systems. It analyzes bus occupancy data, provides scenario-based optimization, and generates decision support reports. Key features include scenario comparison, vehicle and driver assignment, express trip suggestions, and feasibility analysis, all integrated for seamless multi-feature use.

The project aims to develop a GIS-based decision support system to enhance public transportation efficiency and reduce private vehicle use. By optimizing public transport operations with real-time data, including bus occupancy and boarding information, the system will improve capacity analysis, line and route planning, and overall urban transportation management.

The project aims to establish an integrated data infrastructure to analyze accident mechanisms and develop an AI-based platform. It will cluster traffic zones by accident density and provide risk predictions and innovative accident hot spot analyses.

The project aims to create behavioral, preventive, and predictive traffic insights by analyzing data from various systems and sensors using big data methods. It seeks to develop a knowledge base and an interoperable platform to manage incidents, emergencies, and strategies, providing solutions to urban traffic issues with functions like dynamic intersection and centralized traffic management.

The project aims to develop traffic management algorithms at microscopic and mesoscopic levels using big data, sensor integration, and machine learning to optimize urban traffic flow. It will collect real-time data to optimize signal durations at intersections and integrate this with corridor-wide optimizations. The goal is to reduce stops, delays, travel time, fuel consumption, and carbon emissions on main city roads.

The project aims to expand Traffic Electronic Supervision Systems (TEDES) nationwide to enhance traffic safety and reduce accidents amid increasing vehicle numbers and traffic density. It will develop technologies for detecting speed and parking violations, including a Speed Violation Detection System and a Parking Violation Detection System, to improve traffic order and minimize violations.