Probabilistic Elevator Dispatching for Passenger Flow Optimization
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Solution Overview
Problem
Smart elevator systems face inefficiencies in optimizing elevator car utilization, as they largely respond ad-hoc to individual requests without effectively predicting passenger destinations or grouping passengers to minimize stops at different floors.
Innovation Solution
A method and system for probabilistic destination determination in smart elevator car management, which predicts passenger destinations based on sensor data, calendar entries, and historical usage patterns to group passengers and assign them to elevator cars, optimizing car utilization by minimizing stops at different floors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional ad-hoc elevator control systems are used to respond to individual requests, then the system is simple to operate, but elevator car utilization efficiency is poor and the number of stops is excessive
Solution Approach 1:
The system performs preliminary actions by predicting passenger destinations before actual elevator requests are made. The control system analyzes historical data, calendar information, and sensor data to forecast which floors passengers are likely to destination to, enabling proactive elevator dispatching and grouping strategies that optimize car utilization before passengers even press buttons.
Solution Approach 2:
The system implements feedback mechanisms by continuously collecting sensor data from the building, analyzing calendar information, and monitoring historical elevator usage patterns. This feedback loop enables the control system to refine its destination predictions and adjust grouping strategies in real-time, improving elevator car utilization through data-driven decision making.
2Loss of time
If smart elevator systems group passengers by predicted destinations, then the number of stops is reduced and efficiency improves, but the system requires complex data processing and prediction algorithms
Solution Approach 1:
The control system segments passengers into different groups based on their predicted destinations and assigns them to different elevator cars. This segmentation strategy allows the system to optimize each car's route independently, reducing the total number of stops across the bank of elevators and minimizing passenger waiting and travel time.
Solution Approach 2:
The system changes operational parameters by transitioning from traditional floor-button-based control to destination-based control. By analyzing multiple parameters including historical usage patterns, calendar information, and real-time sensor data, the system dynamically adjusts elevator dispatching parameters to optimize travel time and reduce stops.
3Productivity
If probabilistic destination determination is implemented using sensor data and calendar information, then passenger flow optimization improves, but the system requires integration of multiple data sources and complex algorithms
Solution Approach 1:
The control system achieves multi-functionality by integrating multiple data collection and processing capabilities into a single unified system. The system simultaneously handles sensor data acquisition, calendar information parsing, historical usage analysis, and real-time elevator dispatching, making the control system a universal platform that performs multiple functions to optimize passenger flow efficiency.
Data Source
AI summary
Embodiments of the present invention provide a method, system and computer program product for smart elevator car destination management according to probabilistic destination determination. In an embodiment of the invention, a method for smart elevator car destination management according to probabilistic destination determination includes predicting a set of passengers requesting use of an elevator car in a bank of elevator cars in a building and determining a probability for each of the passengers that each passenger will select as a destination a particular floor in the building. The method also includes grouping ones of the passengers in the set according to a common floor determined to be probable for the grouped ones of the passengers. Finally, the method includes displaying in connection with the bank of elevator cars an assignment of the grouped ones of the passengers to one of the elevator cars in the bank.