Production-Environment-Oriented Innovative Approach for Next-Generation Traffic Flow Prediction Using Machine Learning and Deep Learning

The dynamics of global traffic flow in urban environments are undergoing transformations due to the coexistence of autonomous and manual vehicles. Efficient traffic management and prediction are now imperative in this evolving landscape. This research paper provides a comprehensive investigation of traffic flow prediction, emphasizing the integration of machine learning and deep learning techniques in Industry 4.0 to address challenges in mixed traffic scenarios. Through meticulous analysis of publicly available datasets, the study uncovers critical challenges and biases inherent in real-world traffic scenarios in a changing paradigm. By conceptualizing appropriate ‘Real-Time Traffic Flow Prediction Architecture’ and based on our ongoing work of applying ML and DL techniques on publicly available traffic flow data from Northern Virginia/Washington DC capital region, the insights presented in this paper aim to empower researchers, practitioners and policymakers to navigate the intricate landscape of traffic flow prediction, contributing to the evolution of modern transportation systems. This study serves as a cornerstone for fostering innovation and informed decision-making, contributing to the changing transportation ecosystem.