Generating rental data for car sharing relocation simulations on the example of station-based one-way car sharing

Abstract

Developing sophisticated car sharing simulations is a major task to improve car sharing as a sustainable means of transportation, because new algorithms for enhancing car sharing efficiency are formulated using them. Simulations rely on input data, which is often gathered in car sharing systems or artificially generated. Real-world data is often incomplete and biased while artificial data is mostly generated based on initial assumptions. Therefore, developing new ways for generating testing data is an important task for future research. In this paper, we propose a new approach for generating car sharing data for relocation simulations by utilizing machine learning. Based on real-world data, we could show that a combined methods approach consisting of a Gaussian Mixture Model and two classification trees can generate appropriate artificial testing data.