Spatio-Temporal Crowd Routing Scheduling
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Solution Overview
Problem
Existing crowd management systems face challenges in predicting and mitigating crowd dynamics, especially in large gatherings, due to conflicting factors like congestion, environmental conditions, and government regulations, leading to safety risks such as stampedes and casualties.
Innovation Solution
A system that divides event paths into segments, collects data on participant itineraries, crowd behavior, and environmental factors, and uses scheduling algorithms to generate routing solutions that can be dynamically updated to avoid congestion and safety hazards, incorporating user preferences and real-time simulation for effective crowd management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If crowd management systems monitor all factors affecting crowd movement, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The system segments crowd movement factors into distinct categories (congestion, environmental conditions, structural conditions, government regulations, individual itineraries) and processes each separately through specialized modules, allowing comprehensive monitoring while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The crowd management system integrates multiple monitoring functions into a unified platform that handles diverse data types (crowd density, weather, structural occupancy, regulatory compliance) through a single multi-functional system, improving prediction accuracy without proportionally increasing complexity
2Reliability
If routing solutions are dynamically modified in response to crowd changes, then crowd safety improves, but operational complexity increases
Solution Approach 1:
The system pre-establishes multiple routing solutions and contingency plans before events begin, allowing rapid deployment of pre-planned safe routes when crowd conditions change, improving safety responses without the complexity of real-time optimization
Solution Approach 2:
The routing solutions are designed to be dynamically adjustable with predefined modification rules that automatically activate based on crowd condition thresholds, enabling safe adaptive responses through automated decision-making rather than complex manual operations
3Productivity
If scheduling algorithms account for multiple conflicting factors, then routing effectiveness improves, but computational complexity increases
Solution Approach 1:
The scheduling algorithm processes conflicting factors in sequential stages, first evaluating congestion levels, then environmental conditions, then structural constraints, and finally regulatory requirements, breaking down complex computational problems into manageable segments that improve routing effectiveness while controlling computational load
Solution Approach 2:
The system applies different levels of computational analysis to different routing decisions based on local conditions, using simplified models for low-risk situations and more complex analysis only when necessary, optimizing computational resource allocation while maintaining routing effectiveness
Data Source
AI summary
Approaches for determining scheduling assignments for the movement of people along a multi-segment path from a starting location to a destination location, are used to manage crowds, predict crowd behavior, and mitigate crowd turbulence. For example, to mitigate crowd congestion, routing solutions specifying an amount of time to spend at a destination and a departure time can be provided. Itinerary assignments, crowd data, and data associated with an event can be analyzed and weighted to determine scheduling assignments. Scheduling assignments can be validated against current crowd data and event data. Current crowd data and event data and crowd simulation can be used to predict future crowd behavior or crowd problems. Scheduling assignments can be rescheduled to mitigate crowd problems or emergencies.