  Modelling realistic human behaviours in simulation is an ongoing challenge
that resides 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 a path, which is
decisive especially on a leisure trip. Two novel algorithms have been presented
to generate human-like trajectories based on environmental features. The
Attraction-Based A* algorithm includes in its computation information from the
environmental features meanwhile, the Feature-Based A* algorithm also injects
information from the real trajectories in its computation. The human-likeness
aspect has been tested by a human expert judging the final generated
trajectories as realistic. This paper presents a comparison between the two
approaches in some key metrics like efficiency, efficacy, and hyper-parameters
sensitivity. We show how, despite generating trajectories that are closer to
the real one according to our predefined metrics, the Feature-Based A*
algorithm fall short in time efficiency compared to the Attraction-Based A*
algorithm, hindering the usability of the model in the real world.
