Autonomous vehicles congestion model: A transparent LSTM-based prediction model corporate with Explainable Artificial Intelligence (EAI)

Abstract

Urban traffic congestion presents a range of vital difficulties requiring precise prediction models in order to facilitate traffic management for Autonomous Vehicles. This work introduces a novel framework that regulates a Long Short-Term Memory (LSTM) system with methods provided by Explainable Artificial Intelligence (XAI) to explain traffic congestion behavioural modes. For enhanced accuracy and transparency, the integration of EAI methodologies with LSTM based models is addressed as a novel approach towards congestion prediction, while significant research has been done previously using Machine Learning that compared previous proposed based model congestion monitoring improvement through Federated Learning Waqas et al. [18]. This wok proposes the enhances ML focused on Long Short-Term Memory with EAI (LSTM-EAI) model for Smart City environments that require accurate traffic congestion rate forecast to improve the urban mobility. The proposed model provides better interpretability that help stakeholders to understand how the input plays an important role in the condition of traffic jams. The results show that the LSTM-EAI model is 5 % better than previous methods for both the accuracy and reliability of congestion prediction, and may become a practical and effective solution for the urban traffic problem.