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

Christian Roth, Mirja Nitschke, Matthias Hörmann, Doğan Kesdoğan

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.

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