Dataset of Mazovian (Poland) Road Network for Graph Neural Networks
Description
This dataset was developed for the project of analyzing the transport network in the Mazowieckie Voivodeship and comprises a wide range of traffic-related information. The data were collected from various sources, including road technical quality and road incident data from the Polish General Directorate for National Roads and Motorways, travel time information from Google Maps, data obtained from reverse geocoding, population density data from the GUS database, and specific weather conditions for roads. Key Features of the Dataset: Multidimensional Information: The dataset includes information on the date, days of the week, holidays, time (in minutes), and various temporal parameters (T1 - T24). Road and Node Identifiers: Each record contains identifiers for the road (roadId), the start node (start_node), and the end node (end_node). Traffic Factors: It includes key traffic information such as the traffic factor (trafficFactor), midlongitude and midlatitude of the road segment (midLongitude, midLatitude), and details about the number of lanes, road width, presence of two-way traffic (two_ways), and traffic density (density). Weather Conditions: The dataset accounts for various weather conditions, including heavy rain, partial rain, no rain, partial clouds, heavy clouds, clear sky, storms, and fog. Prediction Outcomes: Data include results on traffic speed (result_speed) and conditions such as shuttle traffic (result_shuttle), full (result_fullyclosed) and partial (result_partiallyclosed) road closures, and the presence of traffic lights (result_trafficlight). Data Collection Period: Traffic data were collected from May 25, 2022, to June 22, 2022, providing a comprehensive view of traffic conditions over a nearly one-month period. Data Preparation Process: The collected data were unified and processed to create one large CSV file. This file was then divided into 384 smaller files, each representing the state of the transport network at a specific moment. This dataset forms a comprehensive basis for analyzing and forecasting traffic conditions, offering extensive possibilities for use in machine learning models.