基于克里金法的地磁数据插值研究

地磁场在很多方面都有应用,特别是在目前地磁匹配导航逐渐成为新兴的水下载体导航手段背景下,地磁数据的测量与其插值方法的研究逐渐受到了学者们的关注。文章使用某海区的实测磁异常数据,首先等间隔的取大间距数据应用克里金等插值方法生成等值线图,然后与原始数据小间距的等值线图比较,说明插值方法的有效性。然后进一步改进克里金法,通过交叉验证方法说明克里金法在地磁数据的插值分析中是完全可行有效的。     

后疫情时代基于XGBoost的铁路客运站客流量预测研究

进入"后疫情时期",铁路客流正逐步回升,但呈现较大波动,面对铁路提质增效的任务,准确预测客流量愈发重要.文章采用极端梯度提升(XGBoost,eXtreme Gradient Boosting)模型,以新冠肺炎疫情、天气和日期属性作为影响因素,选取上海站2016年1月1日—2020年7月27日客流量数据作为训练集和验证...     

Decision tree method for modeling travel mode switching in a dynamic behavioral process

As road congestion is exacerbated in most metropolitan areas, many transportation policies and planning strategies try to nudge travelers to switch to other more sustainable modes of transportation. In order to better analyze these strategies, there is a need to accurately model travelers’ mode-switching behavior. In this paper, a popular artificial intelligence approach, the decision tree (DT), is used to explore the underlying rules of travelers’ switching decisions between two modes under a proposed framework of dynamic mode searching and switching. An effective and practical method for a mode-switching DT induction is proposed. A loss matrix is introduced to handle class imbalance issues. Important factors and their relative importance are analyzed through information gains and feature selections. Household Travel Survey data are used to implement and validate the proposed DT induction method. Through comparison with logit models, the improved prediction ability of the DT models is demonstrated.