Using artificial intelligence and image processing methods to increase pedestrian transportation and ensure their safety

Pedestrians, one of the basic elements of transportation, are the most vulnerable users of traffic. On the other hand, the global energy crisis and the increase in air pollution in urban have again demonstrated that pedestrian transportation and micro mobility vehicles should be preferred over the use of motor vehicles for short distances. Increasing pedestrian transportation and providing safer transportation opportunities to pedestrians is one of the most important policies of developing countries. In this thesis, in order to examine pedestrian transportation, air pollution, fuel prices, pedestrian crossing design, and traffic accidents involving death or injury to pedestrians are discussed. As a result of the analyses made by using machine learning, geographical information systems and artificial neural networks in the structure of intelligent transportation systems, the share of the transportation sector in air pollution, problems in pedestrian crossings, and the most effective factors in pedestrian death or injury accidents were determined. After these analyses, a deep learning-based model, which is an artificial intelligence method, was developed to ensure safe crossing of pedestrian roads at intersections, which is one of the most common problems experienced by pedestrians in wheelchairs. The data set used to train the model was increased by image processing. Thanks to the model, individuals in wheelchairs crossing the pedestrian path were detected with the YOLO, and the object tracking process was realized with the model hybridized with Deepsort. In the last part of the study, as a result of the findings obtained, the thesis process was completed by presenting solutions for increasing pedestrian transportation and ensuring their safety.