Lutfi Al-Sharif received his Ph.D. in elevator traffic analysis in 1992 from the University of Manchester. He worked for 9 years for London Underground, London, United Kingdom in the area of lifts and escalators. In 2002, he formed Al-Sharif VTC Ltd, a vertical transportation consultancy based in London, United Kingdom.
In 2006, he co-founded the Mechatronics Engineering Department at the University of Jordan, Amman Jordan and progressed to full professor at the University of Jordan, where he spent 13 years as a faculty member, Mechatronics Engineering Department Head for six years and Vice Dean for Academic Affairs.
His research interests include elevator traffic analysis, elevator and escalator energy modelling, mechatronics education, coordinate measuring machines and linear electromagnetic actuators. He is co-inventor of four patents, has around 30 papers published in peer reviewed journals and is co-author of the 2nd edition of the elevator traffic handbook.
Professor Al-Sharif is currently Vice President of Al Hussein Technical University in Amman, Jordan, and a part-time consultant for Peters Research Ltd. He is also a member of the management committee of the lift and escalator symposium.
Previous work has established that the average number of up stops and down stops in a building during a round trip, as well as the ratio between them, could be used to estimate the mix of traffic prevailing in the building and its intensity.
Further work has used basic correlation methods to derive the mix of traffic in the building, finding the ratios of incoming traffic, outgoing traffic, interfloor traffic. These studies have assumed that inter-entrance traffic is zero.
This paper builds on the methodologies developed in the earlier work by introducing machine learning techniques to model the relationship between stop types and their locations within a building. The methodology requires knowledge of the types of floors in the building (occupant floors or entrance/exit floors).
The data required for machine learning will be generated in larger amounts in a reasonable time and with modest processing power, whereby the data is representative of a specific building.
Using machine learning in order to estimate the traffic mix in a building from the stops data.
Professor Lutfi Al-Sharif¹ ², Dr Richard Peters², Matthew Appleby².
¹Al Hussein Technical University, Jordan, ²Peters Research Ltd, UK.