Towards a Real Vehicular Data Cloud

Intelligent Transportation Systems (ITSs) increasingly rely on cloud computing to gather and process vehicular data, enabling the development of adaptable transportation solutions. However, the availability of such data is often limited by privacy, security, and commercial concerns, challenging the open research in ITS. Existing datasets rarely include both intra-vehicular data (created from or inside the vehicle) and extra-vehicular data (created from surrounding sources) or provide flexible scenarios for data extraction. This paper introduces a methodology to collect and publish ITS data, including intra and extra-vehicular sensor data. A quantitative analysis was conducted as part of the data characterization to show the associated data challenges. Moreover, the dataset collected is freely accessible, allowing innovation in ITS research and application development.