Commute Path Optimization Using Travel Graph Decomposition
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Solution Overview
Problem
Existing technologies face challenges in efficiently searching or comparing points within a transportation system based on travel paths of entities, particularly in optimizing commute paths between multiple points while considering various constraints.
Innovation Solution
The method involves receiving requests for commute paths that specify travel within a transportation system, determining descriptions of travel, and responding with results obtained using these descriptions. This includes using optimization techniques such as enumeration search, tree search, gradient descent search, and branch-and-bound search to solve optimization problems related to commute paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If optimization techniques such as enumeration search, tree search, gradient descent search, and branch-and-bound search are used to solve optimization problems related to commute paths, then the accuracy of travel duration calculations is improved, but the computational complexity and time required for processing increases
Solution Approach 1:
The patent segments the commute path optimization problem into multiple components: receiving requests with commute path specifications, determining travel descriptions through optimization techniques, and responding with results. This segmentation allows different optimization algorithms to be applied to specific sub-problems, improving accuracy while managing computational complexity through modular processing
Solution Approach 2:
The patent performs preliminary actions by pre-processing commute path specifications and organizing travel data before applying optimization techniques. This includes structuring the input data with entity travel specifications and junction information, which prepares the problem for more efficient optimization processing and reduces computational burden during the actual optimization phase
2Adaptability or versatility
If multiple entities and junctions are considered in commute path specifications, then the comprehensiveness of travel optimization is improved, but the difficulty of detecting and measuring optimal paths increases
Solution Approach 1:
The patent introduces an intermediary processing layer that handles the complexity of multiple entities and junctions. This intermediary system receives commute path specifications, processes the relationships between multiple entities traveling to/from junctions, and applies optimization techniques to determine travel descriptions. This intermediary approach simplifies the detection and measurement of optimal paths by systematically managing the complexity of multi-entity travel scenarios
3Reliability
If optimization problems with free points are solved by examining all possible selections, then the completeness of results is improved, but the loss of time for processing increases
Solution Approach 1:
The patent applies partial action by examining only the necessary selections of free points rather than all possible combinations. The optimization techniques (enumeration search, tree search, gradient descent, branch-and-bound) are designed to explore the solution space efficiently, examining only those selections that can lead to optimal or near-optimal commute paths. This reduces processing time while maintaining result completeness by avoiding redundant examinations of suboptimal configurations
Data Source
AI summary
The invention concerns searching or comparing points, based on travel of entities among some of the points within a transportation system. Embodiments include a real estate search engine, where a family can search or compare homes and schools, based on commute durations, considering that an adult wants to walk a child to a stop of a school bus. Embodiments also include approaches for solving an underlying optimization problem based on: an enumeration search, a tree search, a gradient descent search, and a branch-and-bound search. An embodiment scales a solution, by decomposing the optimization problem into independent subproblems, using a combinatorial optimization algorithm, that is applied to a certain travel graph. Scaling is also facilitated by a sparsification approach, that generalizes a routing method prevalent in prior art. Some of the approaches are part of a live real estate search engine available to users in South Korea and Japan.