ARCHITECTURE OF FOUR-ARM INTERSECTION PERFORMANCE EVALUATION SYSTEM BASED ON TURN MOVEMENT COUNT AND OMNIDIRECTIONAL CAMERA
Traffic management involves the systematic regulation of the movement of people and vehicles to enhance safety, efficiency, and mobility. Area Traffic Control Systems (ATCS), as a component of traffic management, are designed to regulate traffic flow at intersections by coordinating vehicle movem...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/87631 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Traffic management involves the systematic regulation of the movement of people
and vehicles to enhance safety, efficiency, and mobility. Area Traffic Control
Systems (ATCS), as a component of traffic management, are designed to regulate
traffic flow at intersections by coordinating vehicle movements across different
arms and reducing potential conflicts. Intersections represent critical points of
convergence within the road network. Previous research has developed many
methods for controlling ATCS. However, there are still few studies that focus on
using omnidirectional cameras and evaluating their accuracy and processing
speed. Hence, this research focuses on developing a robust vehicle detection
methodology that balances high accuracy and high frame rate using an
omnidirectional ATCS camera. This research uses a case study approach at the
Sedayu intersection in Yogyakarta, collecting data with omnidirectional CCTV
cameras. The data, including vehicle turning movements, was analyzed using video
analytics over two days: a weekday and a weekend. This study evaluates the
robustness of various single-stage model architectures, focusing on both accuracy
metrics (confidence level, mAP, precision, F1-Score, Recall) and processing speed
(FPS, processing time, GPU memory, GPU load, GPU temperature). The objective
is to identify a model that balances high accuracy with real-time processing
capabilities. The results indicate that YOLOv11, with a batch size of 32,
demonstrates robust real-time performance while maintaining reasonable
accuracy. However, the findings also reveal consistent suboptimal performance,
with Level of Service ratings of E and F, underscoring the need for strategic
interventions in traffic signal control and lane configuration. |
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