Elevator Dispatching Personalization via Frustration Profiling
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Solution Overview
Problem
Existing elevator systems cannot customize dispatching algorithms to meet individual passenger preferences, leading to potential frustration due to factors like wait time, number of passengers, and travel conditions, as passengers cannot provide feedback on their preferences.
Innovation Solution
The system collects frustration indicators and factors, correlates them to generate passenger profiles, and adjusts elevator operations to reduce frustration by prioritizing preferences such as fewer passengers and optimal travel times, using sensors and a computing system to implement personalized control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If elevator dispatching algorithms focus on reducing average waiting time, then overall system efficiency is improved, but individual passenger preferences and frustration levels cannot be addressed
Solution Approach 1:
The system segments the general passenger population into individual users with distinct preference profiles. Each passenger receives personalized elevator dispatching based on their specific frustration factors and preferences, rather than applying a uniform dispatching algorithm to all users. This segmentation enables the system to address individual needs while maintaining overall system efficiency.
Solution Approach 2:
The dispatching algorithm transitions from a static, one-size-fits-all approach to a dynamic, adaptive system that learns and adjusts to individual passenger preferences over time. The system continuously updates user profiles based on collected frustration indicators and modifies dispatching decisions accordingly, making the algorithm flexible and responsive to individual needs.
2Adaptability or versatility
If the system collects and processes frustration indicators for each passenger, then personalized service is achieved, but system complexity increases
Solution Approach 1:
The system employs sensors and computing resources already present in modern elevators to automatically collect frustration indicators without requiring additional manual input from passengers. The existing infrastructure is leveraged to perform data collection, processing, and profile generation, minimizing the need for additional complex components while enabling personalized service.
3Measurement precision
If multiple frustration factors are monitored and correlated, then accurate passenger preferences are generated, but measurement and detection difficulty increases
Solution Approach 1:
The system uses multi-functional sensors and computing resources that serve multiple purposes: monitoring elevator operation, collecting passenger feedback, detecting frustration indicators, and generating preferences. By making existing components multi-functional, the system achieves accurate preference measurement without proportionally increasing detection and measurement complexity.
Data Source
AI summary
A method of controlling an elevator system incudes collecting one or more frustration indicators of a passenger during usage of the elevator system by the passenger; collecting one or more frustration factors; correlating the one or more frustration factors to the one or more frustration indicators to generate one or more preferences for the passenger; generating a profile for the passenger in response to the correlating, the profile including the one or more preferences; and controlling operation of the elevator system in response to the one or more preferences to reduce frustration of the passenger during interaction with the elevator system.