Trip Trajectories of Vehicles Using Map-Matched GPS Points - Spain 2023

The dataset contains 830 957 trips made by vehicles in Spain, recorded throughout the calendar year 2023.

The trip trajectory data was generated by matching telemetrics, hardware installed directly in the vehicles, with street axes.

The vehicles are both passenger cars and light commercial vehicles (LCV).

The data contains the start and end dates and times of the trip, along with a list of (longitude, latitude) pairs for the trajectory. The data is grouped by vehicles (with specific numbers assigned in this dataset), but all data has been anonymized in order to blur the exact origin and destination of the individual vehicles.

The data does not contain any personal data or identifier of the vehicles themselves, their owners or drivers.

This dataset is useful for:

• Precise Route Mapping: This helps generate detailed maps of the exact routes taken by vehicles, creating accurate visual representations for navigation and geographic analysis.
• Urban Planning and Infrastructure Development: This assists in analyzing vehicle routes to inform city planning and infrastructure projects, identifying high-traffic areas and optimizing public transportation routes.
• Traffic Flow Analysis: This enables the study of vehicle movement patterns on street axes to understand traffic congestion and flow, aiding in better traffic management strategies.
• Determining the routes taken by each vehicle: This helps in understanding travel patterns and optimizing routes.
• Real-time or historical tracking of vehicle locations: This is crucial for fleet management, logistics, and ensuring the safety of vehicles.
• Generating alerts: Alerts can be created if a vehicle moves outside predefined boundaries.
• Historical data analysis: This provides insights into trends, such as peak traffic times or common routes.
• Improving customer service: It helps in providing better estimates for arrival times and enhancing overall customer service.

1. tracker_num:
   • Description: Unique identifier for each vehicle being tracked. This identifier is used to distinguish between different vehicles in this dataset. Each entry corresponds to a specific vehicle, labeled as Vehicle_1, Vehicle_2, etc.
   • Type: String
2. start_date:
   • Description: The date and time when the trip started, recorded in Coordinated Universal Time (UTC). The format is YYYY-MM-DD HH:MM:SS.
   • Type: Timestamp
3. end_date:
   • Description: The date and time when the trip ended, recorded in Coordinated Universal Time (UTC). The format is YYYY-MM-DD HH:MM:SS.
   • Type: Timestamp
4. trajectory_coordinates_lon_lat:
   • Description: A list of geographic coordinates representing the trajectory of the vehicle during the trip. Each coordinate pair is represented as (longitude, latitude). The coordinates trace the path taken by the vehicle from the start to the end of the trip. To anonymize the origins and destinations, a random number of initial and final coordinates, totaling a distance of 500 to 800 meters, have been removed.
   • Type: List of tuples (float, float)

Metadata

All location data has been recorded in compliance with GDPR and is provided in an anonimyzed form. Subscription Verification Request Information

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