IoT Metro Scheduling Optimizes Departure Intervals
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current metro operation scheduling methods lack efficiency in optimizing departure intervals based on real-time passenger flow data, leading to potential congestion and reduced operational efficiency.
Innovation Solution
An Internet of Things (IoT) system comprising a user platform, service platform, management platform, sensor network platform, and object platform is implemented to optimize metro operation scheduling. This system collects passenger flow data from multiple metro stations, predicts passenger flow at target stations, and determines optimal departure intervals using machine learning models and reinforcement learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional fixed scheduling methods are used for metro operations, then operational simplicity is maintained, but metro operation efficiency deteriorates due to inability to adapt to real-time passenger flow variations
Solution Approach 1:
The patent implements dynamic scheduling by continuously adjusting metro departure intervals based on real-time passenger flow data. The system transitions from fixed static schedules to dynamic adaptive schedules that respond to changing passenger demand, thereby improving metro operation efficiency without requiring overly complex manual intervention
Solution Approach 2:
The patent establishes a feedback loop where passenger flow data is continuously collected from stations, analyzed by prediction models, and used to adjust departure intervals. This closed-loop control system automatically adapts scheduling based on actual passenger demand, resolving the contradiction between efficiency improvement and system complexity through automated feedback mechanisms
2Use of energy by moving object
If departure intervals are extended to reduce operational costs, then energy consumption decreases, but passenger waiting time increases leading to reduced service quality
Solution Approach 1:
The patent dynamically changes the departure interval parameter based on predicted passenger flow. During high-demand periods, shorter intervals are maintained to reduce waiting time; during low-demand periods, intervals are extended to reduce energy consumption. This adaptive parameter adjustment resolves the contradiction between energy efficiency and service quality
Solution Approach 2:
The system performs preliminary prediction of passenger flow using machine learning models before adjusting departure intervals. This advance prediction allows the system to proactively optimize both energy consumption and waiting time by preparing appropriate schedules before peak demand occurs, rather than reactively responding to congestion
3Measurement precision
If real-time passenger flow monitoring is implemented, then scheduling accuracy improves, but system complexity and data processing requirements increase
Solution Approach 1:
The patent introduces an intermediary IoT platform that consolidates data collection, processing, and analysis functions. This intermediate layer handles the complexity of real-time monitoring and communicates simplified scheduling decisions to the metro operation system, thereby improving measurement accuracy without proportionally increasing overall system complexity
Solution Approach 2:
The IoT platform performs multiple functions including data collection from various stations, passenger flow prediction, scheduling optimization, and real-time monitoring. By consolidating these diverse functions into a single multi-functional platform, the system achieves high measurement precision without requiring separate complex systems for each function
Data Source
AI summary
Methods for optimizing metro operation scheduling in a smart city are provided. The method may be realized by an Internet of Things system for optimizing metro operation scheduling in a smart city including a user platform, a service platform, a management platform, a sensor network platform, and an object platform. The method may be executed by the management platform, and may comprises: obtaining, based on the object platform, passenger flow data of at least one metro station related to a target station by the sensor network platform; determining predicted passenger flow data of the target station in a target period of time based on the passenger flow data of the at least one metro station; and determining an operation scheduling scheme of the target station in the target period of time based on the predicted passenger flow data, the operation scheduling scheme including at least a metro departure interval.