# Comparative Analysis of Human Movement Prediction: Space Syntax and Inverse Reinforcement Learning

arXiv: https://arxiv.org/abs/1801.00464 · HTML: https://mlsanity.com/paper/1801.00464/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Social robot navigation, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 0 of its 14 references that are in the map.
- First posted on arXiv 1 Jan 2018.

## Placed in

- Offline RL and imitation learning from data / [Social robot navigation](https://mlsanity.com/offlinerl/social-robot-navigation/)

## Abstract (arXiv)

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.