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Preference Guided Iterated Pareto Referent Optimisation for Accessible Route Planning

Paolo Speziali, Arno De Greef, Mehrdad Asadi, Willem Röpke, Ann Nowé, Diederik M. Roijers

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.

Preference Guided Iterated Pareto Referent Optimisation for Accessible Route Planning

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.

Paper Structure

This paper contains 20 sections, 6 equations, 5 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background
  3. Multi-objective Shortest Path Planning
  4. Finding preferred Pareto optimal path
  5. IPRO
  6. Method
  7. Preference-Guided IPRO (PG-IPRO)
  8. Referent selection
  9. Closest-distance heuristic
  10. Middle-distance heuristic
  11. Oracle: MO-DFS with pruning and upper-bound guidance
  12. Manhattan Distance Function
  13. Chebyshev Achievement Scalarization Function (ASF)
  14. Interactive loop
  15. Experiments
  16. ...and 5 more sections

Figures (5)

  • Figure 1: Interface design for interactive route planning.
  • Figure 2: Iteratively update the objective search space in IPRO (Figure from ropke2024divide)
  • Figure 3: Average maximum utility comparison between PG-IPRO and GPPE for a convex and a concave Pareto front consisting of 30 solutions (noise=0.01)
  • Figure 4: The seven possibly optimal routes in Osdorp-Midden. The blue marker (above) indicates the origin and the red marker (below) the destination. Each colored path corresponds to one Pareto-undominated solution with objective vector $[d,c]$, where $d$ denotes route length (in metres) and $c$ the number of crossings. The yellow-green route on the extreme left corresponds to $[928,3]$, while the orange route extending furthest to the right corresponds to $[1335,2]$. In the central region, the pink and brown routes in the upper part correspond to $[703,4]$ and $[603,5]$, respectively, and the red route in the lower central area corresponds to $[586,6]$. The two routes closest to the lower boundary are the dark blue route $[568,8]$ and the light blue route $[574,7]$.
  • Figure 5: Average maximum utility for Osdorp-Midden PF with 7 solutions (noise=0.01)