Connected Traffic Systems Based on Referenced Landmarks as Part of Conventional Road Infrastructure

Alexander Jäggle (), Marcel Voßhans (), Michael Probst (), Nils Mursinsky (), Andre Vaskevic, Tobias Heisig (), Reiner Marchthaler () and Ralf Wörner ()
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Alexander Jäggle: Hochschule Esslingen
Marcel Voßhans: Hochschule Esslingen
Michael Probst: Hochschule Esslingen
Nils Mursinsky: Hochschule Esslingen
Andre Vaskevic: Hochschule Esslingen
Tobias Heisig: Volkmann Straßen- und Verkehrstechnik
Reiner Marchthaler: Hochschule Esslingen
Ralf Wörner: Hochschule Esslingen

Chapter Kapitel 20 in Transforming Mobility – What Next?, 2022, pp 339-353 from Springer

Abstract: Abstract Due to the increasing number of traffic participants, especially in urban areas, traffic flow planning is becoming increasingly complex. The associated problems could be solved with the help of connectivity and new disruptive communication strategies. From the point of view of traffic econometrics, connected driving means that the status of each road user is known and thus recommendations for actions can be made. For this purpose, traffic data must first be collected, which is achieved by classical counting (e.g., radar systems), and then be analyzed with the help of descriptive statistics (e.g., hydrographs). However, counting systems have to be installed and maintained in a decentralized manner. Alternatives are provided by analyzed smartphone data (e.g., Google Maps API) that influence the traffic flow via indirect control elements (e.g., congestion messages). However, the existing systems have either drawbacks regarding data protection or too high workload. This paper presents a new concept based on referenced landmarks (machine-readable traffic signs), which can be useful as a control tool for complex traffic control systems. Here, conventional traffic signs are coated with a layer, which can only be read by infrared cameras. The content of this layer is a code (QR code) optimized for machine readability, which contains a unique ID and the meaning of the traffic sign. The ID can then be used to retrieve additional information about the sign from a central database (e.g., cloud). Thus, the exact position of the traffic sign recommended trajectories or current traffic information can be retrieved. Each database query detects that a vehicle has passed the sign, thus the traffic situation can be fully mapped. This information can passively and anonymously serve as input parameters for a traffic guidance system.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-658-36430-4_20

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DOI: 10.1007/978-3-658-36430-4_20

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