Deep Learning Based Mobile Platform for Predicting Passenger Congestion in Public Transportation

Population growth in Turkey has led to significant congestion in transportation systems. This congestion results in delays for individuals using public transportation, often causing them to arrive hours early to ensure punctuality at their destinations. Passengers frequently lack real-time information about the current occupancy of public transportation vehicles, leading to uncertainty and potential inefficiencies as they wait at bus stops. In some cases, public transportation vehicles reach full capacity, leaving passengers at the stop unserved. Moreover, it is crucial for passengers to be aware of the crowd levels at various stops, as this affects their ability to efficiently board and travel, particularly on smaller vehicles such as minibuses where stop density is a key consideration. As a result, passengers must make informed decisions based on both vehicle and stop density, as well as the estimated vehicle arrival time. To address these challenges, we propose a deep learning-based mobile platform designed to detect crowd density at bus stops and within public transportation vehicles. This platform will notify both the transit manager and the passengers about real-time density information. Our approach involves analyzing 20 -minute video footage from Mersin Municipality, Turkey, using Mask-RCNN,YOLOv5, YOLOv8, and YOLOv9 models to count passengers boarding and alighting. The models were evaluated on accuracy, precision, sensitivity, F1 score, Frame Per Second (FPS), and Intersection Over Union (IOU) metrics. The experimental results indicated that the YOLOv9 model significantly outperformed the other models, achieving $88 \%$ accuracy, $86.2 \%$ precision, $89 \%$ recall, 15.2 fps, and 0.78 IoU.