噪声条件下的稳健出租车费用预测：GAT、TimesNet与XGBoost的比较研究
Robust Taxi Fare Prediction Under Noisy Conditions: A Comparative Study of GAT, TimesNet, and XGBoost
AI总结：
本文在含5500多万条记录的出租车数据集上比较GAT、TimesNet与XGBoost，并结合去噪和多种预处理策略评估其准确性、校准、不确定性及OOD稳健性。
摘要：
精确的费用预测对于网约车平台和城市出行系统至关重要。本研究考察了三种机器学习模型——图注意力网络（GAT）、XGBoost和TimesNet，利用一个包含超过5500万条记录的真实数据集评估它们对出租车费用的预测能力。研究分析了数据集的原始（含噪）版本和去噪版本，以评估数据质量对模型性能的影响。研究从多个维度评估模型，包括预测准确性、校准、不确定性估计、分布外（OOD）稳健性和特征敏感性。我们还探讨了预处理策略，包括KNN插补、高斯噪声注入和基于自动编码器的去噪。研究揭示了经典模型与深度学习模型在现实条件下的关键差异，为在城市费用预测系统中构建稳健且可扩展的模型提供了实用指南。
英文摘要：
Precise fare prediction is crucial in ride-hailing platforms and urban mobility systems. This study examines three machine learning models-Graph Attention Networks (GAT), XGBoost, and TimesNet to evaluate their predictive capabilities for taxi fares using a real-world dataset comprising over 55 million records. Both raw (noisy) and denoised versions of the dataset are analyzed to assess the impact of data quality on model performance. The study evaluated the models along multiple axes, including predictive accuracy, calibration, uncertainty estimation, out-of-distribution (OOD) robustness, and feature sensitivity. We also explore pre-processing strategies, including KNN imputation, Gaussian noise injection, and autoencoder-based denoising. The study reveals critical differences between classical and deep learning models under realistic conditions, offering practical guidelines for building robust and scalable models in urban fare prediction systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
