Use of Machine Learning for Expanding Realistic and Usable Routes for Data Analysis on Sustainable Mobility

Fabian Schirmer, Andreas Freymann, Anamaria Cristescu, Niklas Geisinger

2021

Abstract

The current mobility or the transition to more sustainable alternatives are constantly changing. For Promoting a sustainable mobility and for investing in a proper infrastructure, we need accurate data regarding the mobility behavior. Gathering location information such as GPS can help to improve the charging infrastructure and the bicycle or pedestrian paths. This motivates the citizens to use sustainable means of transportation such as bicycles or electric cars. However, using personal information via GPS data can cause some challenges: preserving data privacy while keeping data quality to get useful analysis results. This paper presents an advanced approach of processing GPS data based on machine learning and spatial cloaking in contrast to current approaches focusing on common algorithms only. The evaluation has been conducted by generating simulated GPS trips. As a result, the presented approach provides an algorithm that prevents a complete loss of useful data while protecting the privacy of each user in cases where cloaking areas are close together.

Paper Citation

in Harvard Style

Schirmer F., Freymann A., Cristescu A. and Geisinger N. (2021). Use of Machine Learning for Expanding Realistic and Usable Routes for Data Analysis on Sustainable Mobility. In Proceedings of the 6th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS, ISBN 978-989-758-504-3, pages 156-163. DOI: 10.5220/0010395201560163

in Bibtex Style

@conference{iotbds21,
author={Fabian Schirmer and Andreas Freymann and Anamaria Cristescu and Niklas Geisinger},
title={Use of Machine Learning for Expanding Realistic and Usable Routes for Data Analysis on Sustainable Mobility},
booktitle={Proceedings of the 6th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,},
year={2021},
pages={156-163},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010395201560163},
isbn={978-989-758-504-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 6th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,
TI - Use of Machine Learning for Expanding Realistic and Usable Routes for Data Analysis on Sustainable Mobility
SN - 978-989-758-504-3
AU - Schirmer F.
AU - Freymann A.
AU - Cristescu A.
AU - Geisinger N.
PY - 2021
SP - 156
EP - 163
DO - 10.5220/0010395201560163