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 used machine learning techniques to model the relationship between stops data and their locations within a building with the type of traffic mix. The methodology required knowledge of the types of floors in the building (occupant floors or entrance/exit floors), and used stops data in the up and down direction on occupant floors and entrance/exit floors to infer the mix of traffic in the building. However, previous work was based on one specific building.
This paper extends the previous work in order to show that that relationship is independent of the following:
• Number of entrance floors and occupant floors.
• The car capacity.
Such a universal relationship would allow the use of this derived set of equations for any building regardless of the number of floors and passenger numbers.
For this purpose, the research in this paper will employ four different sizes of buildings in terms of number of occupant floors and entrance/exit floors, as well as the variation in the car capacity (i.e., number of passengers in each round trip).
The methodology will rely on the principle of normalisation. Moreover, a lift traffic simulator will be used to generate the data required for training the machine learning model.
ESTABLISHING A UNIVERSAL RELATIONSHIP BETWEEN THE TRAFFIC MIX IN A BUILDING AND THE STOPS DATA
Professor Lutfi Al-Sharif¹ , Tahani Ghaben¹, Dr Richard Peters², Matthew Appleby².
¹Al Hussein Technical University, Jordan, ²Peters Research Ltd, UK.