Cognitive Elevator Dispatching for Predictive Traffic and User Feedback
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Solution Overview
Problem
Current elevator dispatching systems only optimize for immediate traffic conditions without considering future traffic or user preferences, and lack user interaction and feedback, leading to inefficiencies in elevator allocation.
Innovation Solution
A system that collects user data and elevator usage data to construct a predictive dispatch model, which anticipates future elevator needs and refines itself based on user feedback, using machine learning algorithms to optimize elevator allocation in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If current traffic-based dispatching is used, then real-time elevator arrangement is achieved, but future traffic is not considered
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing user behavior data to predict future traffic patterns before they occur. The dispatching model uses historical data and machine learning to anticipate future elevator demand, allowing the system to proactively optimize elevator schedules rather than merely reacting to current conditions.
2Productivity
If traditional dispatching systems are used, then traffic optimization is achieved, but user preferences are not understood
Solution Approach 1:
The system implements feedback by collecting user behavior data and preferences through input devices, then using this information to refine the dispatching model. User interactions and usage patterns are continuously fed back into the machine learning algorithm, enabling the system to adapt and improve its understanding of individual user preferences while maintaining high elevator efficiency.
3Extent of automation
If closed dispatching systems are used, then automated control is achieved, but user feedback cannot be collected
Solution Approach 1:
The system achieves universality by integrating multiple functions into a single platform: automated dispatching control, user data collection, preference analysis, and feedback processing. The unified system can simultaneously perform automated elevator management while also gathering and utilizing user feedback, eliminating the need for separate manual feedback mechanisms.
4Loss of time
If predictive modeling is implemented, then future traffic prediction is achieved, but system complexity increases
Solution Approach 1:
The system applies self-service by using machine learning algorithms that automatically learn from historical data and improve predictions over time without requiring manual intervention. The model autonomously adjusts its parameters and refines its accuracy, reducing the need for complex manual configuration and maintenance while delivering accurate future traffic predictions.
Data Source
AI summary
A method and system for controlling elevator dispatch is provided. User data, including user behavior, is collected from a number of users over a specified time period. Elevator use data for a number of elevators in a building is also collected over the specified time period. Applying the user data and elevator use data, an elevator dispatch model is constructed that predicts future elevator use according to predicted user needs. An elevator control system dispatches the elevators according to the dispatch model. The elevator dispatch model is refined according to feedback data collected from users over a subsequent time period.