Hierarchical Task Network planning with common-sense reasoning for multiple-people behaviour analysis

Abstract

Safety on public transport is a major concern for the relevant authorities. We address this issue by proposing an automated surveillance platform which combines data from video, infrared and pressure sensors. Data homogenisation and integration is achieved by a distributed architecture based on communication middleware that resolves interconnection issues, thereby enabling data modelling. A common-sense knowledge base models and encodes knowledge about public-transport platforms and the actions and activities of passengers. Trajectory data from passengers is modelled as a time-series of human activities.Common-sense knowledge and rules are then applied to detect inconsistencies or errors in the data interpretation. Lastly, the rationality that characterises human behaviour is also captured here through a bottom-up Hierarchical Task Network planner that, along with common-sense, corrects misinterpretations to explain passenger behaviour. The system is validated using a simulated bus saloon scenario as a case-study. Eighteen video sequences were recorded with up to six passengers. Four metrics were used to evaluate performance. The system, with an accuracy greater than 90% for each of the four metrics, was found to outperform a rule-base system and a system containing planning alone.

A decision support framework for security resource allocation under ambiguity

“…(i) Some psychological studies on different attitudes to ambiguity can be done to confirm our different the ambiguity‐aversion instantiations. (ii) The event reasoning framework 61,62 can be used as a platform to check the power of our work in practice. (iii) It is also significant to study games with complicated fuzzinesses modelled by Pythagorean fuzzy set, 63,64 Fermatean fuzzy sets 65 and MBS theory 56 .…”

A knowledge graph-based method for epidemic contact tracing in public transportation

“…Studies in the health domain mainly focus on providing intelligent healthcare solutions but have rarely involved epidemiology ( Elvira et al, 2019 ), while studies in the mobility domain mainly focus on urban transportation, e.g., the Km4City ontology adopted by the Sii-Mobility project ( Bellini and Nesi, 2018 ), which supports a series of research studies on different urban traffic issues (( Badii et al, 2019 , Badii et al, 2018 , Bellini et al, 2014 )). However, in the specific domain of public transportation, some studies have applied the functional components of the knowledge graph independently, such as knowledge reasoning ( Santofimia et al, 2017 ) and knowledge representation ( Liang et al, 2018 ), but an efficient framework that integrates data and models of public transportation for pandemic contact tracing using knowledge graphs has not yet been developed.…”