Participatory Crowdsensing for the Collection of Pickup and Delivery Data

Datasets collected from pickup and delivery applications allow for innovation in the field of Intelligent Transportation Systems (ITS). However, the current challenge in the field is the limited access to such datasets, as they are only collected by large commercial platforms. In this paper, we propose a method of data collection to create effective pickup and delivery datasets. We leverage the hybrid allocation model of the Snap’N’Go crowdsensing platform and re-engineer some of its components to leverage it for data collection, information extraction, and verification. Thus, developing a robust, controllable, and scalable data collection platform that allows researchers in the field to collect and publish their own datasets in the future.