The increasing capability of ingesting crowdsensing data from vehicle fleets for a wide variety of applications on the basis of road conditions, is producing ever-increasing amounts of data. This needs to be aggregated in order to add value and reduce the quantity of data. In the case of spatial data, various clustering methods are available that serve this purpose. The main objective of this paper is to develop a method that provides lane-accurate spatio-temporal data on road anomalies on the basis of fleet data for the first time. To achieve this, a DBSCAN algorithm is applied in conjunction with an Affinity Propagation algorithm to cluster additional parameters. 95.7% of all single obstacles were classified as true positives using the DBSCAN algorithm. Moreover, additional metadata for classification using Affinity Propagation, resulted in a true positive rate of 99%.
Spatio-Temporal Clustering of Road Obstacles
07.03.2022
1061045 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Spatio-temporal clustering for non-recurrent traffic congestion detection on urban road networks
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