Modeling PKT at a global level: A machine learning approach

TL;DR

This paper develops a machine learning model to predict passenger-kilometers-travelled (PKT) globally, analyzing key macroeconomic factors like GDP and population demographics to understand their impact on country-level mobility.

Contribution

It introduces a panel-based regression approach to quantify how macroeconomic variables influence PKT growth across countries.

Findings

GDP and population growth significantly affect PKT.

Macroeconomic variables explain a substantial portion of PKT variation.

The model provides insights for global mobility planning.

Abstract

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.