Elevator Predictive Dispatching Based on Passenger Travel Speed
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional elevator systems in high-rise buildings require passengers to take multi-segment trips and learn building layouts, leading to inefficiencies due to varying travel speeds of individuals, which are not accounted for in dispatching algorithms.
Innovation Solution
A system that interfaces with mobile devices to predictively schedule elevator arrivals based on individual travel speeds, learning user paces through metadata collection and adjusting for conditions like congestion and weather, enabling hands-free navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional elevator dispatching algorithms use average travel speed for all passengers, then the system is simple to operate, but it creates inefficiencies because individual travel speed variations are not accounted for
Solution Approach 1:
The system performs preliminary actions by collecting travel speed data from mobile devices before the elevator dispatching decision is made. The processing system measures travel times between locations and calculates individual travel speeds in advance, storing this information for later use in predictive dispatching algorithms, thereby enabling personalized dispatching without adding operational complexity.
Solution Approach 2:
The system implements feedback by continuously monitoring actual travel speeds of passengers using mobile devices and using this feedback to refine and update individual travel speed profiles. This feedback loop allows the dispatching algorithm to adapt to changing passenger behaviors and conditions, improving efficiency while maintaining algorithmic simplicity through data-driven adjustments.
2Measurement precision
If the system collects and processes individual travel speed data from mobile devices, then dispatching accuracy improves, but the system complexity increases
Solution Approach 1:
The system uses mobile devices as intermediaries to collect travel speed data. Instead of installing complex sensing infrastructure throughout the building, the patent leverages the mobile devices that passengers already carry to measure travel times and calculate speeds, thereby achieving precise measurements without significantly increasing system complexity.
Solution Approach 2:
The system implements self-service by utilizing the mobile devices and their existing sensors (GPS, accelerometer, gyroscope) to automatically collect and process travel speed data without requiring additional dedicated measurement infrastructure. The mobile devices perform the measurement functions themselves, reducing the complexity burden on the central elevator control system.
3Loss of time
If the dispatching system predicts individual arrival times based on personal travel speeds, then passenger wait time reduces, but the extent of automation increases
Solution Approach 1:
The system performs preliminary action by calculating and storing individual travel speed profiles before dispatching decisions are needed. This pre-computed data enables the automated system to quickly predict arrival times without complex real-time calculations, reducing passenger wait time while keeping the automation level manageable through pre-prepared data structures.
Solution Approach 2:
The system applies parameter changes by using personalized travel speed parameters instead of generic average speeds for each passenger. This parameter customization enables more accurate arrival time predictions, reducing waiting time while the automation handles the complexity of managing multiple individual parameters through systematic data storage and retrieval mechanisms.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
An aspect includes a system with a communication interface configured to receive a mobile device location indicator at a first location and a second location. The system also includes a memory system and a processing system configured to perform operations. The operations (300) include detecting (310) a mobile device at the first location and observing (320) a current state of one or more conditions present during detection of the mobile device. A travel prediction of the mobile device between the first location and the second location is determined (330) based on a travel history of the mobile device and the current state of the one or more conditions. A command is output (340) to a dispatching system based on detection of the mobile device at the first location and the travel prediction of the mobile device to align with a predicted arrival of the mobile device at the second location.