Baudains, PeterHolliman, Nicolas S.Hunter, DavidSlingsby, Aidan2024-09-092024-09-092024978-3-03868-249-3https://doi.org/10.2312/cgvc.20241233https://diglib.eg.org/handle/10.2312/cgvc20241233Smart Cities are characterised by their ability to collect and process large volumes of sensor data. Visual analytics is then often required to make this data actionable and to allow decisions to be made in support of the well-being of inhabitants. In this study, using Bus Open Data, we consider how space-time clustering can be used to generate visual summaries of traffic congestion. Using a space-time extension of DBSCAN, our clustering procedure is evaluated with respect to both Euclidean distance and street network distance. Results show that network-based distance metrics improve the clustering procedure by generating clusters with less uncertainty. Moreover, congestion clusters derived from network-based distances are also more likely to last longer and to precede future congestion appearing nearby. We suggest that network-based distances might provide greater opportunity for more impactful traffic control room decision-making and we discuss steps towards a near real-time system design that can be used in support of operational decision-making.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing → Visual analytics; Geographic visualization; Information visualization; Information systems → Clustering; Sensor networks; Data analyticsHuman centered computing → Visual analyticsGeographic visualizationInformation visualizationInformation systems → ClusteringSensor networksData analytics10.2312/cgvc.202412339 pages