Abstract
The control of urban traffic signals typically works on the basis of predefined plans or as a centralised planning system. At least in research work, a locally organised, self-adaptive approach has been established as a more robust, scalable and efficient alternative. In all three cases, the best case scenario is that the system reacts to observed current situations - but no incidents such as accidents, construction work or road blockages of varying duration and extents are detected and considered as a basis for control decisions. In this article, we present an approach for cluster-based detection of such disturbances without the need to extend the existing infrastructure. Based on our previous approach, additional urban road networks are evaluated, all comprised of intersections equipped with programmable traffic signals. An additional incident type, where not all lanes of a road are blocked, is assessed. The underlying traffic flow data is generated in simulations of varying traffic volumes.
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Thomsen, I., Tomforde, S. (2022). Detecting Extended Incidents in Urban Road Networks for Organic Traffic Control Using Density-Based Clustering of Traffic Flows. In: Klein, C., Jarke, M., Helfert, M., Berns, K., Gusikhin, O. (eds) Smart Cities, Green Technologies, and Intelligent Transport Systems. VEHITS SMARTGREENS 2021 2021. Communications in Computer and Information Science, vol 1612. Springer, Cham. https://doi.org/10.1007/978-3-031-17098-0_17
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