A Systematic Decade Review of Trip Route Planning with Travel Time Estimation based on User Preferences and Behavior
It addresses the problem of improving navigation systems for users in complex urban environments, but it is incremental as it synthesizes existing research rather than presenting new results.
This paper systematically reviews advancements in adaptive trip route planning and travel time estimation using AI techniques, identifying key innovations and challenges in the field.
This paper systematically explores the advancements in adaptive trip route planning and travel time estimation (TTE) through Artificial Intelligence (AI). With the increasing complexity of urban transportation systems, traditional navigation methods often struggle to accommodate dynamic user preferences, real-time traffic conditions, and scalability requirements. This study explores the contributions of established AI techniques, including Machine Learning (ML), Reinforcement Learning (RL), and Graph Neural Networks (GNNs), alongside emerging methodologies like Meta-Learning, Explainable AI (XAI), Generative AI, and Federated Learning. In addition to highlighting these innovations, the paper identifies critical challenges such as ethical concerns, computational scalability, and effective data integration, which must be addressed to advance the field. The paper concludes with recommendations for leveraging AI to build efficient, transparent, and sustainable navigation systems.