Airport Fleet Task Assignment Using Predicted Traffic Flow
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Solution Overview
Problem
Operational efficiency of airports is impacted by factors such as weather, staffing availability, flight delays, gate availability, and vehicle availability, leading to inefficiencies in managing fleet vehicles and aircraft operations.
Innovation Solution
An airport operations management system that includes a fleet management device and autonomous fleet vehicles, which utilize an electronic processor to receive operational data, determine tasks, predict traffic flow, and assign tasks to vehicles, navigating them to execution locations with optimized routes based on availability and timing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual fleet management is used, then flexibility in handling unexpected situations is maintained, but operational efficiency and task completion speed deteriorate
Solution Approach 1:
The autonomous fleet vehicles perform baggage transport tasks autonomously without requiring manual control or supervision. The vehicles self-navigate to destinations, self-manage task execution, and self-report status to the central system, eliminating the need for continuous human intervention while maintaining operational flexibility
Solution Approach 2:
The patent replaces manual mechanical control systems with automated electronic control systems. The central management system uses electronic processors to receive operational data, determine tasks, predict traffic flow, and assign tasks to autonomous vehicles, substituting human decision-making with algorithm-based automation
2Productivity
If more fleet vehicles are deployed, then task completion capability is improved, but traffic congestion and coordination difficulty worsen
Solution Approach 1:
The central management system continuously receives real-time data from all fleet vehicles including location, status, and operational information. This feedback loop enables the system to monitor traffic conditions, predict congestion, and dynamically adjust task assignments to optimize fleet distribution and minimize traffic congestion
Solution Approach 2:
The task assignment system is dynamic rather than static. It continuously predicts traffic flow patterns and adjusts task assignments in real-time based on current fleet vehicle locations, traffic conditions, and task priorities. This dynamic approach allows the system to optimize the use of multiple vehicles while avoiding congestion
3Manufacturing precision
If autonomous fleet vehicles are used, then task execution speed and precision are improved, but system complexity and initial investment cost worsen
Solution Approach 1:
The autonomous fleet vehicles are designed as multi-functional units capable of performing various baggage handling tasks including transport, loading, and unloading. The vehicles use standardized interfaces and modular components that can be adapted to different task requirements, reducing overall system complexity while maintaining high execution precision
Solution Approach 2:
The central management system acts as an intermediary between operational requirements and autonomous vehicle execution. It processes operational data, determines appropriate tasks, and translates high-level objectives into specific navigation and execution instructions for the autonomous vehicles, simplifying the control architecture
4Adaptability or versatility
If real-time task reassignment is implemented, then responsiveness to operational changes is improved, but computational load and processing time worsen
Solution Approach 1:
The system pre-calculates and stores predicted traffic flow patterns and optimal routing information based on historical data and current conditions. When task reassignment is needed, the system retrieves pre-computed information rather than performing full real-time calculations, reducing computational energy load while maintaining rapid responsiveness to operational changes
Data Source
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AI summary
Examples provide an airport operations management system including a fleet management device including an electronic processor configured to receive first operational data from an airport operational data source. Based on the first operational data, the electronic processor determines a set of tasks for transporting aircraft baggage, and a predicted traffic flow of a plurality of fleet vehicles and a plurality of aircrafts operating in an airport. Based on the predicted traffic flow, the electronic processor assigns the set of tasks to a fleet device associated with an autonomous fleet vehicle of the plurality of fleet vehicles, and transmits instructions for executing the set of tasks to the fleet device for display on a display screen of the fleet device. The autonomous fleet vehicle includes a transportation system configured to operate according to the instructions for executing the set of tasks.