Crowd-Aware Route Prompting for Crossing-Avoidant Item Pickup
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Solution Overview
Problem
Existing location-based services and data structures do not effectively address the challenge of optimizing pedestrian routes in crowded venues to minimize user crossings and ensure safe, efficient item acquisition.
Innovation Solution
A system and method that utilizes location data, item acquisition lists, and machine learning to generate and evaluate candidate routes based on crossing avoidance and speed factors, selecting the optimal route and prompting users to travel accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional location-based services are used to track users in a venue, then location monitoring is achieved, but route optimization to minimize crossings and ensure safety is not provided
Solution Approach 1:
The system performs preliminary route planning by generating multiple candidate routes before the user actually travels. It evaluates these routes in advance using machine learning models that predict crossing probabilities and travel times, selecting the optimal route beforehand to minimize safety risks and improve efficiency.
Solution Approach 2:
The system continuously monitors user location and compares it against predicted routes of other users. When a potential crossing is detected or predicted, the system provides feedback by adjusting the recommended route or notifying the user, creating a dynamic feedback loop that adapts to real-time conditions to maintain safety.
2Productivity
If multiple candidate routes are generated and evaluated using multiple factors, then route safety and efficiency are improved, but computational complexity increases
Solution Approach 1:
The system changes the parameters used for route evaluation dynamically based on current conditions. Instead of using fixed criteria, it adjusts evaluation parameters such as crossing probability thresholds, travel time weights, and safety factors in real-time based on user behavior patterns, venue congestion levels, and predicted user movements, optimizing the balance between evaluation thoroughness and computational load.
3Measurement precision
If real-time location data is continuously collected and processed, then accurate route prediction is achieved, but data processing requirements and system resource consumption increase
Solution Approach 1:
The system applies partial action by processing location data selectively rather than continuously. It triggers detailed route analysis and machine learning predictions only when necessary - such as when a user enters a new zone, when other users are detected in the vicinity, or when route changes are predicted - rather than continuously processing all location data, thereby reducing computational energy consumption while maintaining accuracy.
Data Source
AI summary
Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: examining location data that specifies respective locations of a plurality of users in a venue, wherein the plurality of users include first through Nth users within the venue, wherein the first through Nth users include at least a first user and a second user; examining an item acquisition list for the first user, wherein the item acquisition list specifies items for acquisition by the first user that are distributed into different locations of the venue; generating a plurality of candidate routes for the first user commencing from a current location of the first user, wherein the plurality of candidate routes include one or more candidate route commencing from the current location of the first user to a first item referred on the item acquisition list.