一项比较研究和简单的基线,用于旅行时间预测
Chuang-Chieh Lin1, Ming-Chu Ho2, Chih-Chieh Hung3
1Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung City, 202301, Taiwan.
Scientific reports
|July 15, 2025
概括
准确的旅行时间预测 (TTP) 在很大程度上依赖于数据预处理. 像LSTM和XGBoost这样的基本模型优于混合方法,从而产生了新的融合模型,以提高TTP准确性.
科学领域:
- 运输工程 运输工程
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 准确的旅行时间预测 (TTP) 对于交通管理和旅行计划至关重要.
- 现有的TTP方法复杂,涉及多个阶段,很难确定影响准确性的关键因素.
研究的目的:
- 调查各种TTP过程步骤对预测准确性的影响.
- 为了比较不同的数据归算和特征工程技术.
- 评估基础模型与TTP的混合模型.
主要方法:
- 评估数据归算技术 (深度学习,插值,最大值).
- 评估时间特征和天气条件的影响.
- 通过使用来自台湾和加利福尼亚的真实世界数据集,比较了五种混合TTP模型和基础模型 (LSTM,XGBoost).
主要成果:
- 数据预处理,包括特征工程,显著影响TTP准确性.
- 基础模型 (LSTM,XGBoost) 在真实数据上表现出高于混合模型的性能.
- 一个通过门网结合XGBoost和LSTM的新型融合模型实现了更高的预测准确性.
结论:
- 数据预处理是TTP准确性的关键决定因素.
- 简单的基础模型可能比复杂的混合模型更有效.
- 拟议的XGBoost-LSTM融合模型为TTP提供了强大而准确的方法.
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