Agents preserving privacy on intelligent transportation systems according to EU law

Abstract

Intelligent Transportation Systems are expected to automate how parking slots are booked by trucks. The intrinsic dynamic nature of this problem, the need of explanations and the inclusion of private data justify an agent-based solution. Agents solving this problem act with a Believe Desire Intentions reasoning, and are implemented with JASON. Privacy of trucks becomes protected sharing a list of parkings ordered by preference. Furthermore, the process of assigning parking slots takes into account legal requirements on breaks and driving time limits. Finally, the agent simulations use the distances, the number of trucks and parkings corresponding to the proportions of the current European Union data. The performance of the proposed solution is tested in these simulations with three different distances against an alternative with complete knowledge. The difference in efficiency, the number of illegal breaks and the traveled distances are measured in them. Comparing the results, we can conclude that the nonprivate alternative is slightly better in performance while both alternatives do not produce illegal breaks. In this way the simulations show that the proposed privacy protection does not impose a relevant handicap in efficiency.