Pedestrian Tour Generation Using Arc Scoring and Graph Optimization
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Solution Overview
Problem
Existing systems for generating pedestrian tours are inefficient in terms of computational resources and fail to effectively incorporate user preferences for route length, elevation profile, and scenic quality, often resulting in tours that are either too long or lack pleasantness due to complex computational methods and lack of flexibility.
Innovation Solution
A method that uses a processor to generate personalized pedestrian tours by scoring arcs based on user preferences, optimizing tour length, and avoiding complex representations, allowing for dynamic adjustments and flexibility in elevation and scenic quality, while minimizing computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If complex computational methods such as external constraint programming solvers are used to optimize tour scores, then the quality and pleasantness of the generated tours is improved, but the computational resources required and processing time increase significantly
Solution Approach 1:
The tour generation process is divided into separate modules: graph construction from OSM data, arc scoring based on multiple criteria (length, elevation, traffic), and tour optimization using specialized algorithms. This segmentation allows each module to be optimized independently, reducing overall computational complexity while maintaining tour quality.
Solution Approach 2:
The system pre-calculates and stores arc scores based on multiple criteria before tour generation. Elevation data, traffic information, and geometric properties are computed in advance and cached, so that during actual tour generation, only combination and selection operations are needed, significantly reducing real-time computational demands.
2Adaptability or versatility
If the system incorporates multiple user preferences including elevation profile, tour length, and scenic quality, then the adaptability and personalization of tours is improved, but the device complexity and computational requirements increase
Solution Approach 1:
Different arc segments are evaluated using different criteria based on user preferences. For example, arcs with steep elevation changes are penalized differently than flat arcs, and arcs near points of interest receive different scoring than ordinary segments. This localized evaluation allows multiple preferences to be incorporated without uniformly increasing complexity across the entire system.
Solution Approach 2:
The system uses adjustable parameters to control the influence of different user preferences. By changing parameter values (such as elevation penalty weights, maximum tour length, or scenic importance), the system can adapt to different user needs without structural modifications, maintaining simplicity while achieving high versatility.
3Reliability
If the system generates closed tours that return to the starting location with specific length constraints, then the reliability and user satisfaction are improved, but the computational resources and time required increase
Solution Approach 1:
The closed tour constraint is handled by extracting and specializing algorithms for closed-loop routing. Instead of treating closed tours as a special case of general routing, the system uses dedicated algorithms that inherently enforce the closed-loop constraint, reducing the computational overhead of checking and enforcing this constraint during generation.
Solution Approach 2:
The tour generation system dynamically adjusts parameters such as acceptable length margins and elevation constraints during the optimization process. This dynamic adaptation allows the system to find valid closed tours more efficiently by flexibly navigating the solution space rather than rigidly enforcing all constraints from the start, reducing computational time while maintaining reliability.
Data Source
AI summary
Generating a pedestrian tour includes: receiving a query from a user device via a network, the query indicative of a request for a pedestrian tour and including a location and preferences including a target length for the pedestrian tour; obtaining a graph of a geographical area around the location of the user device, the graph including nodes indicative of path crossings and arcs indicative of paths connecting pairs of the nodes; determining scores for the arcs based on characteristics of the arcs, respectively, and the preferences; selecting connecting ones of the arcs based on the scores of the connecting ones of the arcs, lengths of the selected connecting ones of the arcs, and the target length; adding the selected connecting ones of the arcs to the pedestrian tour to initialize the pedestrian tour; and transmitting the pedestrian tour to the user device via the network for display.