DOI: 10.5281/zenodo.10435153
title: TrafficDator Madrid

description: Data Origin: This dataset was generated using information from the Community of Madrid, including traffic data collected by multiple sensors located throughout the city, as well as work calendar and meteorological data, all provided by the Community.

Data Type: The data consists of traffic measurements in Madrid from June 1, 2022, to September 30, 2023. Each record includes information on the date, time, location (longitude and latitude), traffic intensity, and associated road and weather conditions (e.g., whether it is a working day, holiday, information on wind, temperature, precipitation, etc.).

Technical Details:





Data Preprocessing: We utilized advanced techniques for cleaning and normalizing traffic data collected from sensors across Madrid. This included handling outliers and missing values to ensure data quality.




Geospatial Analysis: We used GeoPandas and OSMnx to map traffic data points onto Madrid's road network. This process involved processing spatial attributes such as street lanes and speed limits to add context to the traffic data.




Meteorological Data Integration: We incorporated Madrid's weather data, including temperature, precipitation, and wind speed. Understanding the impact of weather conditions on traffic patterns was crucial in this step.




Traffic Data Clustering: We implemented K-Means clustering to identify patterns in traffic data. This approach facilitated the selection of representative sensors from each cluster, focusing on the most relevant data points.




Calendar Integration: We combined the traffic data with the work calendar to distinguish between different types of days. This provided insights into traffic variations on working days and holidays.




Comprehensive Analysis Approach: The analysis was conducted using Python libraries such as Pandas, NumPy, scikit-learn, and Shapely. It covered data from the years 2022 and 2023, focusing on the unique characteristics of the Madrid traffic dataset.





Data Structure: Each row of the dataset represents an individual measurement from a traffic sensor, including:



id: Unique sensor identifier.

date: Date and time of the measurement.

longitude and latitude: Geographical coordinates of the sensor.

day type: Information about the day being a working day, holiday, or festive Sunday.

intensity: Measured traffic intensity.

Additional data like wind, temperature, precipitation, etc.




Purpose of the Dataset: This dataset is useful for traffic analysis, urban mobility studies, infrastructure planning, and research related to traffic behavior under different environmental and temporal conditions.

Acknowledgment and Funding:



This dataset was obtained as part of the R&D project PID2020-113037RB-I00, funded by MCIN/AEI/10.13039/501100011033.

In addition to the NEAT-AMBIENCE project, support from the Department of Science, University, and Knowledge Society of the Government of Aragon (Government of Aragon: group reference T64_23R, COSMOS research group) is also acknowledged.

For academic and research purposes, please reference this dataset using its DOI for proper attribution and tracking.