Edge Computing for AR Queue Navigation in Mass Transit
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Solution Overview
Problem
Users face difficulties in identifying and selecting the correct queue to board a mass-transit vehicle or access services within transportation hubs, where multiple dynamic queues exist and are influenced by various factors such as vehicle schedules, rider behavior, and service requirements.
Innovation Solution
The system utilizes IoT devices for device-to-device communications and edge computing to analyze and generate augmented reality (AR) information, providing users with contextual data on queue locations, directions, and user information, such as destinations and estimated arrival times, via electronic devices like smartphones or smart glasses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If centralized cloud computing is used to process transportation data, then comprehensive analysis capability is improved, but bandwidth usage increases and security is reduced
Solution Approach 1:
The patent implements edge computing by deploying processing capabilities at local transportation hubs rather than relying solely on centralized cloud computing. Each edge device processes data locally for immediate queue management and AR content generation, while only relevant results are transmitted to the cloud. This localizes processing quality to where it is most needed, reducing bandwidth consumption while maintaining comprehensive analysis capability.
Solution Approach 2:
The system segments the computing architecture into multiple hierarchical levels: edge devices at transportation hubs for local processing, regional servers for coordination, and centralized cloud for overall management. This segmentation allows each layer to handle appropriate tasks independently, reducing the need for constant cloud communication while maintaining system-wide coherence and security.
2Productivity
If real-time AR information is provided to all users, then user navigation efficiency is improved, but information overload and system complexity increase
Solution Approach 1:
The system performs preliminary processing of queue information and AR content generation at the edge devices before users arrive. Queue configurations, AR overlays, and navigation instructions are prepared in advance based on predicted transportation patterns and historical data, so that when users arrive, the information is already optimized and ready for immediate display, reducing real-time processing complexity.
Solution Approach 2:
The patent introduces an intermediary edge computing layer between the complex transportation management system and the end-user devices. This intermediary processes and simplifies raw transportation data into user-friendly AR presentations, filtering out unnecessary information while preserving critical navigation details. The intermediary manages the complexity transformation, presenting simplified information to users without exposing the underlying system complexity.
3Adaptability or versatility
If multiple queues are dynamically managed, then service flexibility is improved, but difficulty in detecting and measuring queue status increases
Solution Approach 1:
The patent replaces manual or mechanical queue monitoring methods with automated computer vision and sensor-based detection systems. Edge devices equipped with cameras and sensors automatically detect queue formation, measure queue lengths, and track user movement through queues in real-time. This substitution of mechanical monitoring with electronic sensing and image processing enables accurate, real-time measurement of dynamic queue status across multiple flexible service points.
Solution Approach 2:
The system implements continuous feedback loops where edge devices monitor queue status, automatically adjust queue configurations and AR navigation instructions, and update users in real-time. The feedback mechanism includes detecting queue length changes, processing speed variations, and user flow patterns, then dynamically reconfiguring queue assignments and providing real-time AR guidance to optimize service flexibility while maintaining manageable monitoring complexity.
Data Source
AI summary
A method for utilizing IoT information to generate queue related augmented reality (AR) information associated with a transportation system. In an embodiment, the method includes at least one computer processor identifying a plurality of users within a staging area of a transportation system. The method further includes determining groups of users from among the plurality of users, based on a transportation route respectively associated with transportation information corresponding to respective users. The method further includes identifying a first vehicle that a first user is scheduled to board, based on an indication of the first vehicle in the transportation information associated with the first user. The method further includes determining AR information related to queues associated with boarding the first vehicle and transportation information corresponding to groups of users within a proximity of the queues associated with boarding the first vehicle, and presenting to the first user, the AR information.