iTLM: A Privacy Friendly Crowdsourcing Architecture for Intelligent Traffic Light Management

Christian Roth, Mirja Nitschke, Matthias Hörmann, Doğan Kesdoğan

2020

Abstract

Vehicle-to-everything (V2X) interconnects participants in vehicular environments to exchange information. This enables a broad range of new opportunities. We propose a self learning traffic light system which uses crowdsoured information from vehicles in a privacy friendly manner to optimize the overall traffic flow. Our simulation, based on real world data, shows that the information gain vastly decreases waiting time at traffic lights eventually reducing CO2 emissions. A privacy analysis shows that our approach provides a significant level of k-anonymity even in low traffic scenarios.

Paper Citation

in Harvard Style

Roth C., Nitschke M., Hörmann M. and Kesdoğan D. (2020). iTLM: A Privacy Friendly Crowdsourcing Architecture for Intelligent Traffic Light Management.In Proceedings of the 9th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-440-4, pages 252-259. DOI: 10.5220/0009831902520259

in Bibtex Style

@conference{data20,
author={Christian Roth and Mirja Nitschke and Matthias Hörmann and Doğan Kesdoğan},
title={iTLM: A Privacy Friendly Crowdsourcing Architecture for Intelligent Traffic Light Management},
booktitle={Proceedings of the 9th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2020},
pages={252-259},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009831902520259},
isbn={978-989-758-440-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - iTLM: A Privacy Friendly Crowdsourcing Architecture for Intelligent Traffic Light Management
SN - 978-989-758-440-4
AU - Roth C.
AU - Nitschke M.
AU - Hörmann M.
AU - Kesdoğan D.
PY - 2020
SP - 252
EP - 259
DO - 10.5220/0009831902520259