Title: BOrdDErGuArd - Proactive Enhancement of Human Performance in Border Control
Grant agreement no: 653676
Duration: 1 June 2015 - 31 October 2018
Links: CORDIS Website ESBF LinkedIn
BODEGA project will investigate and model Human Factors in border control to provide innovative socio-technical solutions for enhancing border guards’ performance of critical tasks, support border management decision-making, and optimize travellers’ border crossing experience. BODEGA will develop a PROPER toolbox which integrates the solutions for easy adoption of the BODEGA’s results by stakeholders in border control. PROPER toolbox which will integrate ethical and societal dimensions to enable a leap of border control towards improved effectiveness and harmonisation across Europe.
The PROPER tools will be co-designed and thoroughly validated with relevant stakeholders and end-users. The work will be carried within the framework of Responsible Research and Innovation to ensure the ethical and societal compatibility of the project work and provided solutions as well as emphasis on the foreseen future with smarter borders. With its focus on in-depth understanding of the human factors in border control and PROPER toolbox, BODEGA will enable a leap of European border guard culture towards professionalism.
BODEGA validated, modular and flexible toolbox will enhance the performance of border control stakeholders - border guards, border authorities and citizens - to create more secure, efficient and effective border crossing, focusing on the borders between Schengen agreement and external countries. A holistic view of the Human Factors with respect to the Smart Borders will be developed. The project focuses on human and organizational factors of border control technologies and processes and examines the effects of introducing innovative technologies into key border guard tasks, traveller’s performance and behaviour and to the total system at different levels and at different border control types: rail, sea and air borders.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 653676