用AI优化出行路线与时间预估，更懂用户偏好和实时路况。
A Systematic Decade Review of Trip Route Planning with Travel Time Estimation based on User Preferences and Behavior
- 融合机器学习、强化学习等AI技术动态规划路线。
- 支持用户偏好与实时交通变化，提升路径推荐准确性。
- 适合智能导航、城市交通规划者参考，关注可解释性与隐私。
本文系统回顾了过去十年基于用户偏好与行为的自适应出行路线规划与行程时间估计（TTE）在人工智能（AI）领域的进展。随着城市交通系统日益复杂，传统导航方法难以应对动态用户需求、实时交通状况及可扩展性挑战。本研究分析了机器学习（ML）、强化学习（RL）、图神经网络（GNNs）等成熟AI技术，以及元学习、可解释AI（XAI）、生成式AI和联邦学习等新兴方法的贡献。同时，论文指出现有关键挑战，包括伦理问题、计算可扩展性及有效数据融合，强调需解决这些问题以推动领域发展。最后提出建议：利用AI构建高效、透明且可持续的导航系统。
原文摘要 · Abstract (English)
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.
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