  Space syntax matrix has been the main approach for human movement prediction
in the urban environment. An alternative, relatively new methodology is an
agent-based pedestrian model constructed using machine learning techniques.
Even though both approaches have been studied intensively, the quantitative
comparison between them has not been conducted. In this paper, comparative
analysis of space syntax metrics and maximum entropy inverse reinforcement
learning (MEIRL) is performed. The experimental result on trajectory data of
artificially generated pedestrian agents shows that MEIRL outperforms space
syntax matrix. The possibilities for combining two methods are drawn out as
conclusions, and the relative challenges with the data collection are
highlighted.
