Towards a human-like movements generator based on environmental features

Modelling realistic human behaviour in simulation is an ongoing challenge that sits between several fields like social sciences, philosophy, and artificial intelligence. Human movement is a special type of behaviour driven by intent (e.g. to get groceries) and the surrounding environment (e.g. curiosity to see new interesting places). Services available online and offline do not normally consider the environment when planning the path, which is decisive especially on a leisure trip. This paper presents a comparison between different generative methods and variations of a path planning algorithm for the task of generating humanlike trajectories based on environmental features. We show how a modified version of the well known A* algorithm outperforms different generative methods by computed evaluation metrics and by a human assessor for the task of generating bike trips in the area around Ljubljana, Slovenia.