Integrated Urban Traffic-Flood (IUTF) Dataset

OverviewThe Integrated Urban Traffic-Flood (IUTF) Dataset is a comprehensive collection of urban traffic and environmental data from 16 diverse cities across<b> Europe, North America, and Asia</b>. These cities include <b>Augsburg, Cagliari, Darmstadt, Essen, Hamburg, Innsbruck, London, Lucerne, Madrid, Manchester, Marseille, Paris, Strasbourg, Taipei, Turin, and Toronto</b>. This dataset uniquely combines traffic flow information, road network data, and rainfall data to provide a robust foundation for studying urban traffic dynamics under various weather conditions, particularly during flood events.Data DescriptionFor each city, the dataset includes the following files:{city}_data_hours.npz: Traffic flow data based on the road network, containing attributes for flow, occupancy, and speed.{city}_distance_hours.csv: Spatial relationship data of the traffic network, with attributes for 'from' node, 'to' node, and distance.{city}_sensor.csv: Sensor data specific to each city.detectors_public.csv: Spatial location data for all traffic sensors.links.csv: Data linking sensors to their respective road network segments.rainfall_data.csv: Rainfall data corresponding to the time periods of sensor measurements.roads.gpkg: Road network data for the area covered by the sensors. Some cities may have multiple .gpkg files if the road network spans multiple regions. These files should be merged for comprehensive analysis. The data is sourced from OpenStreetMap.selected_network_4326.geojson: Road centreline data for the area covered by the sensors.The IUTF Dataset addresses common challenges in urban traffic-flood studies by integrating diverse data types. It offers a unique resource for researchers and practitioners in urban planning, traffic management, and climate resilience. The dataset's innovative features include the transformation of point-based traffic data to road segment attributes and the use of a line-graph topology, providing new possibilities for analysing and modelling complex urban systems. This dataset not only supports the development of advanced traffic prediction models but also facilitates research in urban resilience and traffic management during extreme weather events. It provides a more accurate representation of traffic dynamics and their interaction with environmental factors, which is crucial for developing effective strategies for urban flood resilience.Data SourceThe city traffic flow data in IUTF is from UTD19. UTD19[1] is another significant dataset used in this research, which includes urban traffic data from 40 cities worldwide. The dataset, as described in the UTD19 manual, contains detailed traffic measurements collected from various stationary sensors such as inductive loop detectors, supersonic detectors, cameras, and Bluetooth detectors. These sensors provide data on fundamental traffic variables including flow, speed, and occupancy. However, the dataset does not inherently include the spatial relationships between sensors. To overcome this, we used OSMNX[2] to retrieve OpenStreetMap (OSM) data to map the sensor locations onto the road network. By associating each sensor with its corresponding road segment, we were able to construct a graph network that accurately reflects the spatial relationships between sensors, thus enabling more detailed and context-aware traffic analysis. In addition, weather Data for London is also incorporated into the study to account for environmental factors that might affect traffic flow. This data is sourced from the London Met Office[3] and NW3 weather[4], providing detailed meteorological information such as temperature, precipitation, and wind speed. These variables are crucial for understanding and predicting traffic patterns under varying weather conditions.ReferenceLoder, A., Ambühl, L., Menendez, M. &amp; Axhausen, K. W. Understanding traffic capacity of urban networks. Sci. Rep. 9, 16283 (2019).Boeing, G. Modeling and Analyzing Urban Networks and Amenities with OSMnx.Weather and climate change. Met Office https://www.metoffice.gov.uk/ (2024).Rodgers, B. NW3 Weather - Live and historical weather from Hampstead, London. http://nw3weather.co.uk/.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.