The recent developments in the field of intelligent transportation systems have shown a significant shift towards leveraging deep learning and advanced neural network architectures to address complex traffic management challenges. The focus has been on enhancing real-time data processing capabilities, improving predictive accuracy, and ensuring robust performance across diverse and dynamic urban environments. Innovations in deep learning models, such as transformers and graph neural networks, have been particularly noteworthy for their ability to capture intricate spatio-temporal dependencies and global relations, which are crucial for tasks like trajectory prediction, traffic speed estimation, and urban pathfinding. Additionally, there is a growing emphasis on domain adaptation and transfer learning techniques to improve model generalization across different traffic networks and conditions. Notably, the integration of Bayesian approaches and unsupervised learning methods has shown promise in enhancing the reliability and efficiency of traffic state estimation and air quality monitoring systems. These advancements collectively aim to provide more adaptable, efficient, and safer transportation solutions, contributing to the broader goal of creating smarter and more sustainable cities.