在全局水平上建模PKT：一种机器学习方法
Modeling PKT at a global level: A machine learning approach
摘要：
人们普遍认为，从一个地方到另一个地方的能力，或流动性，大大有助于一个人的福祉。对流动性的需求是普遍的，但对流动性的需求因国家而异。这项特别研究着眼于全球范围内哪些最重要的因素可以帮助预测各国的旅行者旅行或旅行者英里数（PKT/PMT）。这项特别的工作试图量化一些关键变量的影响，如国内生产总值（GDP），人口增长，就业率，家庭数量，人口中的年龄人口统计和宏观经济变量对每个国家内的总车辆出行。一个基于面板的回归模型，以确定一些关键的宏观经济变量对国家的PKT增长的影响。
英文摘要：
It is well-accepted that the ability to go from one place to another, or mobility, contributes significantly to one's wellbeing. The need for mobility is universal, but the demand for mobility shows a great variation on a country basis. This particular study looks at what are some of the most important factors on a global level that can help in predicting the passengerkilometers-travelled or passenger-miles-travelled (PKT/PMT) on a country by country basis. This particular work tries to quantify the impact of some of the key variables like Gross Domestic Product (GDP), population growth, employment rate, number of households, age demographics within the population and macroeconomic variables on the total vehicle-based travel within each country. A panel-based regression model is developed to identify the effect of some of the key macroeconomic variables on the countries' PKT growth.
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