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Preference Guided Iterated Pareto Referent Optimisation for Accessible Route Planning

arXiv cs.AI / 4/2/2026

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Key Points

  • The paper introduces Preference Guided Iterated Pareto Referent Optimisation (PG-IPRO) as a method for urban route planning that accounts for diverse accessibility requirements and user preferences.
  • It enables interactive route optimization by letting users provide feedback to further minimize or relax specific objectives, improving usability—particularly in the early optimization iterations.
  • The approach focuses on iterative optimization rather than computing the full set of alternatives, so it avoids explicitly generating the entire Pareto front and improves computational efficiency.
  • By not computing the full Pareto front, PG-IPRO aims to reduce user waiting times while still producing strong alternative route policies that reflect different accessibility trade-offs.

Abstract

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.

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