Preference Guided Iterated Pareto Referent Optimisation for Accessible Route Planning
arXiv:2604.00795v1 Announce Type: new 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.
arXiv:2604.00795v1 Announce Type: new 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.
Executive Summary
This article proposes a novel algorithm, Preference Guided Iterated Pareto Referent Optimisation (PG-IPRO), designed to facilitate accessible route planning for individuals with diverse accessibility requirements and preferences in urban settings. The algorithm enables users to interact with the system through iterative feedback, allowing for more intuitive decision-making, particularly during initial iterations. By avoiding the computation of the full Pareto front, PG-IPRO achieves higher computational efficiency and reduced waiting times. While the proposed solution addresses pressing issues in urban mobility, its limitations and potential applications require further exploration.
Key Points
- ▸ PG-IPRO algorithm enables user interaction through iterative feedback
- ▸ Improves intuitive decision-making, particularly during initial iterations
- ▸ Enhances computational efficiency and reduces waiting times
Merits
Strength in user-centered design
The algorithm prioritizes user experience and accessibility requirements, ensuring that the generated routes accommodate individual preferences and needs.
Efficient computational approach
By avoiding the computation of the full Pareto front, PG-IPRO reduces processing time and makes it more suitable for real-world applications.
Demerits
Limited scalability
The algorithm's performance and efficiency may degrade as the size of the input data increases, potentially affecting its applicability in larger urban areas.
Data quality dependence
The accuracy and reliability of the generated routes rely heavily on the quality and availability of the underlying data, including mobility patterns and accessibility information.
Expert Commentary
While PG-IPRO shows promise in addressing urban mobility challenges, its limitations, such as scalability and data quality dependence, must be addressed to ensure its widespread adoption. Furthermore, the algorithm's potential applications extend beyond route planning, potentially influencing urban planning and policy-making. As such, further research and development are necessary to fully realize the potential of PG-IPRO.
Recommendations
- ✓ Future research should focus on addressing the scalability and data quality issues associated with PG-IPRO.
- ✓ The algorithm's potential applications in urban planning and policy-making should be explored in greater detail.
Sources
Original: arXiv - cs.AI
