A Study of an Effective Continual Meta-Learning Based Algorithm for Task-Specific and Device Characteristics Data in Traffic Management

Abstract

Intelligent traffic management methods are gaining popularity for their potential to deal with a combination of both highly variable and constantly changing traffic environments, as well as a wide variety of sensors. However, there is growing interest in adaptive learning techniques that can handle changing traffic conditions and device-specific data features due to the complexity of traffic scenarios and the variety of sensor platforms.This paper provides a thorough analysis of continuous meta-learning-based methods for device-aware learning and task-specific adaptation in intelligent traffic management. The review looks at how meta-learning enables quick learning across a variety of traffic management tasks and how constant learning helps models retain previously learned information while adjusting to new traffic patterns.Additionally, the study examines current approaches to deal with data heterogeneity resulting from sensors, cameras, and Internet of Things (IoT) devices, emphasizing their effects on real-time performance, generalization, and model resilience. Based on learning frameworks, traffic applications, datasets, adaption methodologies, assessment criteria, and claimed performance, a comparative review of previous studies is presented.Catastrophic forgetting, domain adaptability across heterogeneous devices, limited benchmark datasets, computational complexity, and scalability for large scale urban traffic networks are among the major issues that the paper highlights.A number of future research directions are discussed based on the results of the reviewed literature, including explainable artificial intelligence, federated learning, multi-modal sensor fusion for next-generation intelligent transportation systems, and continuous meta-learning integration with edge intelligence.This studyhighlights new prospects for creating adaptive, scalable, and robust intelligent transportation solutions while providing researchers and practitioners with an organized grasp of the current status of continuous meta-learning in traffic management.