Applications of Deep Learning in Traffic Management: A Review

Abstract

This research explores the increasing applications of deep learning in traffic management systems, with a focus on traffic prediction, object detection and recognition, autonomous vehicles, traffic flow optimization, and parking management. The results show that deep learning can improve transportation efficiency, reduce congestion, and enhance overall traffic management. One of the significant applications of deep learning in traffic management is traffic prediction. The findings suggest that deep learning models can accurately predict traffic patterns, enabling traffic managers to anticipate congestion and adjust traffic signal timings accordingly. However, one limitation of this application is the need for large amounts of historical data to train the models effectively, which may not be available in some regions or representative of current traffic conditions. The study finds that deep learning algorithms can detect and recognize objects in real-time, optimizing traffic signal timing, reducing collisions, and improving pedestrian safety. Nevertheless, the accuracy of the models can be affected by occlusions or cluttered scenes, particularly in challenging environments such as low-light or adverse weather conditions. Moreover, the research reveals that deep learning is a crucial technology in developing autonomous vehicles. Autonomous vehicles can optimize traffic management by reducing congestion, optimizing routes, and improving overall transportation efficiency. Nonetheless, the models may struggle with handling edge cases, such as unexpected road conditions or obstacles, and require extensive testing to ensure safety and reliability. Additionally, the study suggests that deep learning models can analyze real-time traffic data to optimize traffic flow by adjusting signal timings, predicting traffic patterns, and rerouting traffic. However, one limitation of traffic flow optimization using deep learning is the need for real-time traffic data, which may not be available or accurate, affecting the effectiveness of the models. The findings show that deep learning can be used to identify available parking spaces in real-time, reducing the time and fuel spent by drivers searching for parking spaces. Nevertheless, deep learning models for parking management may struggle with accurately detecting and recognizing vehicles in crowded parking lots or complex parking structures. The cost of implementing the required hardware for real-time detection may also be prohibitive in some locations.