Pareto Optimal Path Selection for Multi-Criteria Journey Planning
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Solution Overview
Problem
Existing journey planning systems fail to consider multiple criteria effectively, such as arrival time, toll cost, and convenience, when optimizing routes, often requiring users to choose itineraries before proposing exit and entry points, which neglects other relevant factors and user preferences.
Innovation Solution
A system and method that compute a set of Pareto optimal candidate paths from an origin to a destination, respecting user-defined constraints on criteria like arrival time, toll cost, and toll crossings, using a network graph that models toll roads and non-toll roads, allowing users to select paths that offer different trade-offs between these criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If a user exits and reenters the tollway to reduce toll costs, then the total toll cost is reduced, but the journey time increases due to queuing at tollbooths and traveling at lower speeds
Solution Approach 1:
The system dynamically adjusts route recommendations based on real-time conditions and user preferences. It provides multiple candidate paths with different trade-offs between toll costs and journey time, allowing users to select their preferred balance rather than forcing a single static solution.
Solution Approach 2:
The system changes the parameters of route optimization by considering multiple criteria simultaneously (toll cost, journey time, number of toll crossings) rather than optimizing for a single parameter. This allows users to adjust their preferences and receive customized route suggestions that reflect their specific needs.
2Reliability
If multiple criteria are considered in route optimization, then the solution quality improves, but the computational complexity increases
Solution Approach 1:
The system segments the route optimization problem into discrete path candidates, each evaluated against multiple criteria. By generating a finite set of candidate paths and ranking them based on user preferences, the system manages computational complexity while maintaining solution quality.
Solution Approach 2:
The system replaces complex mechanical optimization processes with information-based processing. It uses computational algorithms to evaluate multiple criteria and generate ranked path suggestions, substituting physical trial-and-error with efficient information processing and mathematical optimization.
3Ease of operation
If the system requires users to choose an itinerary before proposing exit/entry points, then the planning process is simplified, but other relevant factors and user preferences are neglected
Solution Approach 1:
The system performs multiple functions simultaneously: it generates route candidates, evaluates them against multiple criteria, and provides personalized recommendations based on user preferences. This multi-functional approach eliminates the need for sequential user inputs while comprehensively considering all relevant factors.
Solution Approach 2:
The system autonomously gathers necessary information, generates candidate paths, evaluates them against multiple criteria, and presents optimized suggestions without requiring users to manually specify itineraries first. The system serves itself by automatically handling the complex optimization process while adapting to user preferences.
Data Source
AI summary
A computer-implemented system and method identify Pareto optimal candidate paths between an origin and a destination for which no other candidate path is strictly better on one of a predefined set of criteria and at least as good on all the others. A constraint is defined for each of the criteria, based on user input. A set of Pareto optimal candidate paths is identified, from an origin to a destination, which respect these constraints. The identification may include, in a search graph composed of nodes connected by edges, iteratively advancing each of a set of possible paths from an origin node by exactly one exit node and updating labels of the exit nodes reached. The exit node labels each include a value for each of the criteria. Labels of reached exit nodes that are dominated by another label of that reached node are removed. Pareto optimal candidate path(s) is/are identified.