We provide a standardized graph dataset for traffic based on the large-scale NuPlan v1.1 dataset, converted to a PyTorch-Geometric dataset using our CommonRoad-Geometric tool. The dataset is collected from 3 megacities: Singapore, Boston and Pittsburgh.

We provide a roads dataset that includes the spatial location of roads, the estimated age of each road, and the predicted traffic volume of each road between 1986 and 2020 in Wyoming, USA. Our annual estimates of traffic volume are available for each road and include estimates for all vehicles and truck only traffic. Moreover, we provide the estimated age of each road, where a minimum value of 1986 indicates that the road existed in 1986, and any later year indicates the most likely year that road was developed. This dataset will be beneficial for any research focused on the mechanistic effects of road traffic on wildlife populations. Our roads dataset is based on a comprehensive inventory of paved and unpaved roads in Wyoming of 2015 National Aerial Imagery Program (NAIP) aerial imagery (Fancher et al. 2023). We developed annual estimates of road age and vehicular traffic volume across 147,108 km of highways, arterials, collectors, local, and gravel/graded roads within the state of Wyoming. To assign road age, we leveraged a suite of ancillary data on surface disturbances (e.g., oil and gas drilling operations, wind turbines, and open pit mines) with known establishment dates. Then, we predicted traffic volume for each year across Wyoming using XGBoost, a novel machine learning method, to relate ongoing traffic monitoring by the Wyoming Department of Transportation with a separate set of spatial covariates hypothesized to explain traffic patterns across large regions.

Here we deposit the datasets we have extracted for ten states in the US. In each zip file, we include each state's accident records, road networks, and network features. For further information about using the dataset and how we extracted the data, check out our GitHub repository for instructions.

In the second quarter of 2024, the mobile data traffic reached almost 150 exabytes worldwide, which is an increase of around 50 exabytes compared to the same quarter in the previous year. The global mobile voice traffic has remained the same since the first quarter of 2016, with 0.23 exabytes.

January 2024, Netflix.com generated over 412 million visits in the United States. Traffic to the SVoD platform increased by seven percent compared to the previous month. Overall, Netflix was the leading subscription video-on-demand service in terms of traffic during the examined period. Between the second half of 2022 and the beginning of 2023, search and visit volume trends on streaming sites in the market appeared to have normalized after the usage increase brought by the COVID-19 pandemic in 2020 and 2021.

data and codes

Wireless data traffic surged in the United States in 2023, with more than 100 trillion megabytes of data transferred over mobile networks that year. This was almost twice the volume consumed two years prior, with demand for data soaring amid the adoption of data intensive mobile activities.

France Land Transport Statistics (LTS): Freight Traffic: Road data was reported at 41,258.810 Ton/km mn in Sep 2024. This records a decrease from the previous number of 41,745.930 Ton/km mn for Jun 2024. France Land Transport Statistics (LTS): Freight Traffic: Road data is updated quarterly, averaging 42,476.000 Ton/km mn from Mar 1994 (Median) to Sep 2024, with 123 observations. The data reached an all-time high of 53,324.000 Ton/km mn in Jun 2007 and a record low of 32,398.000 Ton/km mn in Sep 1994. France Land Transport Statistics (LTS): Freight Traffic: Road data remains active status in CEIC and is reported by Ministry for the Ecological Transition. The data is categorized under Global Database’s France – Table FR.TA001: Land Transport Statistics: Freight Traffic. [COVID-19-IMPACT]

Additional attributes to the linear component of the BDTRE traffic graph, relating to the Average Daily Traffic (TGM) on an annual basis and to other information related to vehicular mobility, derived from the comparison with the Regional Supervisory Graph (SVR) managed by 5T srl on behalf of the Region Piedmont. For further details on the production methods of the TGM data associated with the SVR Graph, consult the file (Modello_SVR.pdf) contained in the material being downloaded. 2019 update, annual frequency.

In 2024, smartphones across the globe used an average of 20.29 gigabytes of mobile data per month, up from 17.27 gigabytes the previous year. This figure is expected to reach 23.78 gigabytes in 2025, and 42.38 gigabytes by 2029.

This data shows traffic volumes for freeways (excluding toll roads) and arterial roads in Victoria. The annual average daily traffic volume is provided, including the number of commercial vehicles. The data provided is for the current year, with values derived from traffic surveys or estimates.
About this dataset

Data on road traffic by road and vehicle type, produced by Department for Transport.

OverviewThe Integrated Urban Traffic-Flood (IUTF) Dataset is a comprehensive collection of urban traffic and environmental data from 16 diverse cities across Europe, North America, and Asia. These cities include Augsburg, Cagliari, Darmstadt, Essen, Hamburg, Innsbruck, London, Lucerne, Madrid, Manchester, Marseille, Paris, Strasbourg, Taipei, Turin, and Toronto. 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. & 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/.

The street graph is a topological graph of the section network of Vienna’s streets. The network lines usually run on the road axis. A section of the road is part of a road between two intersections. The stationing direction is carried out according to the ascending orientation numbers. Road sections are limited by grid nodes. These net nodes are usually located at the cross-mechanical point. This dataset extends the public transport areas (municipal roads, main roads A and B).

The road graph is a topological graph of the sectional network of all roads. The network lines usually run on the road axis. A section of the road is part of a road between two intersections. The stationing direction is carried out according to the ascending orientation numbers. Road sections are limited by grid nodes. These net nodes are usually located at the cross-mechanical point

Daily data showing weekday adjusted busyness indices using traffic camera data to monitor flows of cars, pedestrians, cyclists, buses and commercial vehicles for selected cities and regions of the UK. These are official statistics in development. Source: Transport for London, Transport for Greater Manchester

Brazil Highways Statistics: Traffic Tolled: Total Traffic data was reported at 1,834.949 Unit in 2017. This records an increase from the previous number of 1,782.727 Unit for 2016. Brazil Highways Statistics: Traffic Tolled: Total Traffic data is updated yearly, averaging 681.920 Unit from Dec 1996 (Median) to 2017, with 22 observations. The data reached an all-time high of 1,834.949 Unit in 2017 and a record low of 19.564 Unit in 1996. Brazil Highways Statistics: Traffic Tolled: Total Traffic data remains active status in CEIC and is reported by Brazilian Association of Highway Concessionaires. The data is categorized under Brazil Premium Database’s Automobile Sector – Table BR.RAW003: Highways Statistics: Traffic Tolled. The Brazilian Association of Highway Concessionaires-ABCR represents the highway concession sector.

This Dataset contains month-wise city pair-wise passenger traffic statistics for the year 2016

Linear network representing the estimated traffic flows for roads and highways managed by the Ministry of Transport and Sustainable Mobility (MTMD). These flows are obtained using a statistical estimation method applied to data from more than 4,500 collection sites spread over the main roads of Quebec. It includes DJMA (annual average daily flow), DJME (summer average daily flow), DJME (summer average daily flow (June, July, August, September) and DJMH (average daily winter flow (December, January, February, March) as well as other traffic data. It is important to note that these values are calculated for total traffic directions. Interactive map: Some files are accessible by querying a section of traffic à la carte with a click (the file links are displayed in the descriptive table that is displayed when clicking): • Historical aggregated data (PDF) • Annual reports for permanent sites (PDF and Excel) • Hourly data (hourly average per weekday per month) (Excel) • Annual reports for permanent sites (PDF and Excel) • Hourly data (hourly average per weekday per month) (Excel)**This third party metadata element was translated using an automated translation tool (Amazon Translate).**