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

Roth, Christian; Nitschke, Mirja; Hörmann, Matthias; Kesdoğan, Doğan

doi:10.5220/0009831902520259

Cited by 2 publications

(1 citation statement)

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“…There also exist some works that have conducted a performance analysis of STLSs when using PETs. For example, Roth et al [14] applied a pseudonym-based PET on an STLS and analyzed its performance by comparing it with a fixed time traffic lights systems (FTTLSs) and an actuated traffic lights system (ATLS) using inductive loops.…”

Section: State Of the Artmentioning

confidence: 99%

Privacy of Smart Traffic Lights Systems

Hermann¹,

Wolf²,

Trkulja³

et al. 2023

2023 IEEE Vehicular Networking Conference (VNC)

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Smart traffic lights systems (STLSs) are a promising approach to improve traffic efficiency at intersections. They rely on the information sent by vehicles via C2X communication (like in cooperative awareness messages (CAMs)) at the managed intersection. While there exists a large body of work on privacy-enhancing technologies (PETs) for cooperative Intelligent Transport Systems (cITS) in general, such PETs like changing pseudonyms often impact the performance of cITS applications. This paper analyzes the extent to which different PETs affect the performance of two types of STLSs, a phase-based and a reservation-based STLS. These are implemented in SUMO and combined with four different PETs. Through extensive simulations we then investigate the impact of those PETs on STLS performance metrics like time loss, waiting time, fuel consumption, and average velocity. Our analysis shows that the impact of PETs on performance varies greatly depending on the type of STLS. Finally, we propose a hybrid STLS which is a combination of the two STLS types as a potential solution for limiting the negative impact of PETs on performance.

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Section: State Of the Artmentioning

confidence: 99%