论文标题
运输系统中模态拆分预测的可解释的机器学习模型
Interpretable Machine Learning Models for Modal Split Prediction in Transportation Systems
论文作者
论文摘要
运输网络中的模态拆分预测有可能支持网络运营商管理交通拥堵并提高公交服务可靠性。我们专注于每小时预测旅行者使用高维旅行时间数据而不是另一种交通方式的旅行者的问题。我们使用逻辑回归作为基本模型，并采用各种正则化技术进行可变选择，以防止过度拟合并解决多重共线性问题。重要的是，我们解释了模态拆分和旅行者对旅行时间变化的总反应性的固有变化的预测准确性结果。通过可视化模型参数，我们得出的结论是，发现对预测准确性从每小时到小时的变化很重要，并包括拓扑核心和/或高度拥挤的段。我们将方法应用于旧金山湾区高速公路和快速运输网络，并与预先指定的变量选择方法相比，我们的方法具有卓越的预测准确性和解释性。
Modal split prediction in transportation networks has the potential to support network operators in managing traffic congestion and improving transit service reliability. We focus on the problem of hourly prediction of the fraction of travelers choosing one mode of transportation over another using high-dimensional travel time data. We use logistic regression as base model and employ various regularization techniques for variable selection to prevent overfitting and resolve multicollinearity issues. Importantly, we interpret the prediction accuracy results with respect to the inherent variability of modal splits and travelers' aggregate responsiveness to changes in travel time. By visualizing model parameters, we conclude that the subset of segments found important for predictive accuracy changes from hour-to-hour and include segments that are topologically central and/or highly congested. We apply our approach to the San Francisco Bay Area freeway and rapid transit network and demonstrate superior prediction accuracy and interpretability of our method compared to pre-specified variable selection methods.
