NDLI: Improving elevator call time responsiveness via an artificial neural network control mechanism

Content Provider	IEEE Xplore Digital Library
Author	Echavarria, J. Frenz, C.M.
Copyright Year	2009
Description	Author affiliation: Department of Computer Engineering Technology, New York City College of Technology (CUNY), Brooklyn, 11201 USA (Echavarria, J.; Frenz, C.M.)
Abstract	Elevator traffic comprises the movement of individuals from the floor from which they called the elevator to their destination floor. This project seeks to improve elevator call time responsiveness by utilizing the concept that traffic flows generally form definable patterns that can be used to predict future traffic flow behaviors. A feed-forward neural network-based control algorithm has been developed that can approximate elevator call patterns by learning to associate time of day with specific call locations. This algorithm was tested against fuzzy patterns of elevator calls in which the randomly generated calls were biased towards certain floors at certain times of day. When the average neural network controlled call times of 10 such fuzzy sets were compared to the typical scenario of the elevator returning to the first floor after each call, a 42% improvement in elevator call time responsiveness was observed. It is thereby suggested that a machine learning enabled-elevator control system could result in increased user satisfaction by reducing wait times by helping to ensure that the elevator is at the most likely place the elevator will be called from prior to an individual even pushing the call button. The utility of such an algorithm is likely further enhanced, however, by the fact that having the elevator in the most likely call location can also lead to significant energy savings in that the elevator will need to travel less to pick up prospective passengers.
Starting Page	1
Ending Page	3
File Size	268193
Page Count	3
File Format	PDF
ISBN	9781424423477
DOI	10.1109/LISAT.2009.5031561
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Elevators Delay Artificial neural networks Communication system traffic control Neural networks Feedforward systems Feedforward neural networks Testing Test pattern generators Fuzzy sets Neural Networks Control Systems Machine Learning
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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