A Systematic Decade Review of Trip Route Planning with Travel Time Estimation based on User Preferences and Behavior

2025-03-30

AI Summary

Traditional navigation systems struggle with dynamic preference adaptation, real-time responsiveness, and scalability amid increasingly complex urban traffic. Method: This paper systematically reviews dynamic route planning and travel time estimation research (2014–2024) grounded in user behavior and preferences, integrating meta-learning, eXplainable AI (XAI), generative AI, and federated learning for the first time. It leverages graph neural networks, reinforcement learning, and multi-source heterogeneous data modeling to trace technical evolution. Contribution/Results: The study establishes an intelligent navigation evolution framework that balances fairness, interpretability, and sustainability; identifies key ethical risks, scalability bottlenecks, and data fusion challenges; and proposes a concrete implementation roadmap targeting efficiency, transparency, and environmental sustainability.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Adaptive trip route planning for dynamic user preferences
• Travel time estimation using AI techniques in urban transport
• Addressing scalability and ethical concerns in navigation systems

Innovation

• AI techniques like ML, RL, and GNNs
• Emerging methods: Meta-Learning, XAI, Generative AI
• Addressing scalability, ethics, and data integration