Geo-temporal vehicular environmental sensing dataset

Area of Science:

• Environmental Science
• Computer Science
• Urban Planning

Background:

• Urban environments exhibit complex spatio-temporal variability in air quality.
• Mobile sensing platforms offer a method for high-resolution environmental data collection.
• Existing datasets may lack fine-grained, multi-vehicle synchronized measurements.

Purpose of the Study:

• To present a dataset of georeferenced, timestamped environmental measurements (CO₂, temperature) from a mobile sensing fleet.
• To enable research in urban mobile sensing, spatio-temporal analysis, and environmental monitoring.
• To facilitate the development of methods for robust, resource-aware urban monitoring.

Main Methods:

• Utilized three vehicles equipped with low-power, vehicle-mounted sensor nodes.
• Integrated Sensirion SCD41 CO₂ sensors and GPS for georeferencing and timestamping.
• Collected continuous streams of CO₂, temperature, humidity, and speed data along diverse urban routes.

Main Results:

• Generated a comprehensive dataset of synchronized, georeferenced environmental data from multiple mobile platforms.
• Included raw sensor readings, spatial coordinates, temporal metadata, and vehicle identifiers.
• Enabled multi-vehicle trajectory reconstruction and environment-driven analyses.

Conclusions:

• The dataset supports research on urban mobile sensing, spatio-temporal analysis, and environmental monitoring.
• Facilitates benchmarking of interpolation algorithms and validation of mobile sensing strategies.
• Provides a resource for investigating sensor behavior in dynamic conditions and developing urban monitoring methods.