# Preference Guided Iterated Pareto Referent Optimisation for Accessible Route Planning

arXiv: https://arxiv.org/abs/2604.00795 · HTML: https://mlsanity.com/paper/2604.00795/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 17 references that are in the map.
- First posted on arXiv 1 Apr 2026.

## Builds on (most cited first, 2 of 2)

- [Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making](https://mlsanity.com/paper/1802.07606/) (2018)
- [Determining Accessible Sidewalk Width by Extracting Obstacle Information from Point Clouds](https://mlsanity.com/paper/2211.04108/) (2022)

## Abstract (arXiv)

We propose the Preference Guided Iterated Pareto Referent Optimisation (PG-IPRO) for urban route planning for people with different accessibility requirements and preferences. With this algorithm the user can interact with the system by giving feedback on a route, i.e., the user can say which objective should be further minimized, or conversely can be relaxed. This leads to intuitive user interaction, that is especially effective during early iterations compared to information-gain-based interaction. Furthermore, due to PG-IPRO's iterative nature, the full set of alternative, possibly optimal policies (the Pareto front), is never computed, leading to higher computational efficiency and shorter waiting times for users.