Accurately predicting travel time information can be helpful for travelers. This study proposes a framework for predicting network-level travel time index (TTI) using machine learning models. A case study was performed on more than 50,000 TTI data collected from the Washington DC area over 6 years. The proposed approach is also able to identify the effects of weather and seasonality. The performances of the machine learning models were assessed and compared with each other. It was shown that the ridge regression model outperformed the other models in both short-term and long-term predictions.

翻译：准确预测旅行时间信息对出行者具有重要价值。本研究提出一种利用机器学习模型预测网络层面旅行时间指数（TTI）的框架。基于华盛顿特区区域六年内收集的五万余条TTI数据开展了案例研究。所提方法能够识别天气与季节性因素的影响效应。研究评估并比较了各机器学习模型的性能表现，结果表明岭回归模型在短期与长期预测中均优于其他模型。