Automated Wayfinding Design Using Agent-Based Simulation
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Solution Overview
Problem
Conventional wayfinding design in both virtual and real-world environments is manually intensive, time-consuming, and inefficient, as it struggles to optimize navigation scenarios and account for human navigation mistakes and visibility factors, leading to potentially confusing and frustrating experiences for users.
Innovation Solution
An automated computational approach that uses agent-based simulations to optimize wayfinding sign placement, considering path lengths, turn angles, decision points, and human navigation properties, allowing for the generation of optimized wayfinding designs that guide pedestrians effectively and efficiently to their destinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual wayfinding design is used, then design flexibility and customization are improved, but time consumption and labor intensity increase significantly
Solution Approach 1:
The system performs preliminary automated analysis of the 3D environment, pre-calculating navigation paths, visibility zones, and potential wayfinding locations before the actual design process begins. This preliminary computational work reduces the time required for manual design iterations while preserving design flexibility.
Solution Approach 2:
The wayfinding design system performs self-service by automatically generating wayfinding solutions based on the 3D scene analysis, reducing reliance on manual design efforts. The system independently identifies optimal sign placements, evaluates visibility, and proposes navigation aids without requiring extensive human intervention at each design stage.
2Productivity
If automated computational approach is used, then design efficiency and time reduction are improved, but complexity of the system increases
Solution Approach 1:
The automated wayfinding design system is segmented into distinct functional modules: 3D environment analysis, path calculation, visibility evaluation, sign placement optimization, and design generation. Each module handles a specific aspect of the design process, making the overall complex system more manageable and easier to implement while maintaining high productivity.
3Device complexity
If traditional manual design methods are used, then simplicity of the process is maintained, but ability to account for human navigation mistakes and visibility factors is reduced
Solution Approach 1:
The system incorporates feedback mechanisms that simulate human navigation behavior, including typical mistakes and visibility constraints. By modeling how real users navigate and where they commonly make errors, the system iteratively refines wayfinding sign placements to improve navigation accuracy and reliability while accounting for human factors.
4Reliability
If comprehensive optimization considering multiple factors is performed, then navigation experience is improved, but computational requirements and processing time increase
Solution Approach 1:
The system applies local quality by focusing computational optimization on critical areas of the 3D environment rather than uniformly processing the entire space. It identifies regions with poor visibility, complex navigation paths, or high user traffic and concentrates optimization efforts there, reducing overall computational requirements while maintaining high navigation experience in key areas.
Data Source
AI summary
Aspects and embodiments disclosed herein include a computational approach to automatically generate a wayfinding design for a given environment. To use aspects and embodiments of the disclosed computational approach, a designer specifies all the navigation scenarios likely to be taken by the users. A wayfinding design is then generated to accommodate the needs of all the scenarios while considering a number of desirable factors relevant to the navigation experience and management convenience. Through agent-based simulations, the locations of the wayfinding signs are further refined by considering visibility and robustness with respect to the possible mistakes made by the users throughout their navigation. After generating a wayfinding design, the designer can gain further insights of the design by visualizing the accessibility of a destination from any other locations in the environment and remove any blind zones by adding more signs and re-triggering the optimization.