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.
It has long been believed that the number of up stops and down stops in a building, as well as the ratio between them, could be used to estimate the mix of traffic prevailing in the building and its intensity.
With modern lift traffic analysis and data collection methods, it is now possible to generate large amounts of representative data in a reasonable time and with reasonable processing power.
This paper attempts to use the generated data as training and testing data for a machine learning application that could estimate the mix of traffic as well as the intensity of traffic in a building. The method will first be applied to one or more representative buildings and then extended to more general cases.
Using machine learning to estimate the traffic mix and intensity in a building.
Professor Lutfi Al-Sharif¹ ², Dr Richard Peters², Matthew Appleby², Tahani Ghobon¹.
¹Al Hussein Technical University, Jordan, ²Peters Research Ltd, UK.