Public transport security is a major concern in many cities, with robbery being a frequent and alarming issue. This study introduces the Automatic Alert System for Public Transport Robberies based on Deep Learning with Transfer Learning (SAATP-ATP), which focuses on analyzing audio signals. The system utilizes advanced deep learning techniques and transfer learning to enhance accuracy in detecting suspicious activities based on specific sounds associated with robberies. We evaluate the performance of various pre-trained neural network architectures on audio datasets from real robbery incidents in public transport. The system also accounts for environmental and audio quality factors to improve robustness and efficiency. Experimental results demonstrate that SAATP-ATP can identify robbery incidents with high precision, making it an effective tool for enhancing public transport security and providing rapid alerts in emergencies.