Dynamic Transportation Hub Allocation Server
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Solution Overview
Problem
Current transportation systems face inefficiencies in passenger transfer and demand satisfaction due to fixed allocation intervals and limited capacity, leading to delayed travel and missed connections, especially during peak demand or changes in population movement patterns.
Innovation Solution
A transportation system with hubs and connection slots/landing pads, where a server dynamically allocates vehicles or air mobilities based on population movement data, including route and quantity data generated from integrated information like climate, health, trends, and events, to optimize resource allocation and passenger matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If transportation means are allocated at predetermined intervals at terminals, then the system structure is simple and easy to operate, but passenger waiting time increases and the system cannot adapt to abrupt demand changes
Solution Approach 1:
The patent implements dynamic allocation of transportation means by replacing fixed predetermined intervals with real-time demand-based scheduling. The server continuously monitors passenger demand data and adjusts departure times and vehicle allocations dynamically, allowing the system to adapt to abrupt demand changes while maintaining operational simplicity through automated control.
Solution Approach 2:
The system incorporates feedback mechanisms by collecting real-time data on passenger demand, population movement patterns, and transportation status. This feedback is processed by the server to continuously optimize allocation decisions, reducing waiting times while maintaining ease of operation through automated closed-loop control.
2Device complexity
If a fixed number of transportation means are deployed at terminals, then the system is easy to manage, but demand satisfaction decreases during peak periods
Solution Approach 1:
The patent transforms the static fixed-number deployment into a dynamic allocation system where the number of transportation means at each terminal is continuously adjusted based on real-time demand data, population movement patterns, and predicted peak periods. This dynamic approach increases demand satisfaction while managing complexity through automated server-based control.
Solution Approach 2:
The system changes the parameter of transportation means quantity from a fixed constant to a variable that adjusts based on demand conditions. The server modifies allocation parameters in real-time according to observed and predicted demand patterns, enabling the system to scale capacity up or down as needed without manual intervention.
3Device complexity
If transportation means are allocated based on historical data only, then the allocation process is simple, but the system cannot adapt to changing population movement patterns
Solution Approach 1:
The patent implements preliminary action by using the server to predict future demand patterns based on historical data, current conditions, and various external factors (climate, events, health trends). This predictive capability allows the system to proactively adjust allocations before demand changes occur, enhancing adaptability while managing complexity through automated forecasting.
Solution Approach 2:
The system combines historical data analysis with real-time feedback from current demand patterns. The server continuously monitors actual passenger flow and compares it with predictions, adjusting allocations dynamically. This feedback loop enables the system to adapt to changing population movement patterns while maintaining a relatively simple automated process.
Data Source
AI summary
A transportation system including a plurality of hubs, each of the hubs corresponding to an area and including a plurality of connection slots or one or more landing pads, each of the connection slots and the landing pads accommodating a transportation unit; and a server to determine a number or type of transportations units to be allocated to each of the hubs based on movement data related to population movement between the areas and to allocate each of the transportation units to a respective hub.