Traffic Prediction for Efficient Elevator Dispatching

Traffic Prediction for Efficient Elevator Dispatching
Title:
Traffic Prediction for Efficient Elevator Dispatching
Other Titles:
TENCON 2018 - 2018 IEEE Region 10 Conference
Publication Date:
28 October 2018
Citation:
J. Zheng, H. C. Tat Thomas and Y. HuaiBing, "Traffic Prediction for Efficient Elevator Dispatching," TENCON 2018 - 2018 IEEE Region 10 Conference, Jeju, Korea (South), 2018, pp. 2232-2236. doi: 10.1109/TENCON.2018.8650545
Abstract:
Group elevator dispatching has received more and more attentions as its importance for the transportation efficiency of a high-rise building. The major obstacle that prevents the optimization of the elevator dispatching is the uncertain traffic flow of passengers. In this paper, we propose a machinelearning based algorithm to analyze the historical traffic data and then derive a statistical traffic model to represent the generic distribution of traffic flow. Based on the statistical traffic model, all possible dispatching schemes of an elevator group are enumerated. To simulate the cooperation among elevators, a platform with continuous lift movement and coming passengers is built up. The dispatching scheme that can minimize the passengers time cost as well as the total energy consumption of lifts will be selected. The proposed technology would improve the lift efficiency and provide better user experience.
License type:
PublisherCopyrights
Funding Info:
Description:
(c) 2018 IEEE.
ISSN:
2159-3450
2159-3442
ISBN:
978-1-5386-5457-6
978-1-5386-5458-3
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