The design of vertical transportation systems

© Copyright held by the author 2019: Osama Alshhoumi
1
Osama Alshhoumi
Glasgow, Scotland
15/11/2019

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The Design of Vertical Transportation Systems
1. Supervisory control system:
In the history of lift traffic control [1], it is possible to identify five generation in the history of
the control of groups of lifts.
I. 1850-1890 Simple mechanical control
II. 1890-1920 Attendant and electrical car switch control
III. 1920-1950 Attendant/dispatcher and push button control
IV. 1950-1975 Group control: scheduled control to 1960 and ‘zoned’ control
from 1960.
V. 1975 Computer group control.
Nowadays, with the emergent of the industry 4.0 it can be seen that major manufactures in lift
industry transformed to adopt and invest in using the internet of things (IOT) and AI. To gather
as much vertical traffic patterns. For instant, Kone, Otis, Thyssen and Schindler are or have
already been implemented app for smart use by both occupiers and operators. However, there
are building infrastructure challenges to provide the lift with a sufficient communication ports
needed by lifts manufactures to leverage AI, IOT and cloud technology and brings these tools
in robust, secure and scalable agile solution. In [13], Microsoft and Thyssenkrupp elevators
introduced the foundation of intelligent buildings. Moreover, in [14], Schindler introduced the
elevator dynamic environment EDEn by using MATLAB Simulink to enable the end-user to
use the app and makes system simulations with focus on the key issue.
Figure 1 illustrate the four stages of the industrial revolution.
Figure 1

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1.1. Traffic patterns
Figure 2 show the three types of traffic in a building. However, Simple traffic is defined in
terms of the percentage of the building population transported upwards or downwards in five
minutes
Figure 2

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Figure 3
1.2. Lift GROUP CONTROL ALGORITHMS
Group control algorithms can be sub-divided into two main categories as follows:
1.2.1. Up-peak Group Control algorithms: there are different types of the up-peak
group control algorithms. in [2] This control algorithms known as Static
sectoring [2], when the lift arrives at the main entrance, the lift is assigned to a
certain sector. Another type of algorithm known as Dynamic sectoring, shown
in [3], [4], [5], [6] and [7]. Is development of static sectoring, but with the
difference that the size of the sectors changes continuously. In the Destination
group control systems, the advantages of this algorithm, is the lift group logic
has more information, it is possible to make a better allocation decision.
As shown in Figure 4, it can be seen that when using HCA in up-peak AWT
decreased comparing to DSF, COL and ACA ([1],[8], [9], [10], [11], [12]).

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Figure 4
Whereas, in Figure 5 and 6, it can be seen that in down peak and inter-floor
performance, there is no advantages for HCA. Overall, the previous mentioned
algorithms improved the lift handling capacity by boosting the attains value
under up-peak scenario conditions.
Figure 5
Figure 6

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1.2.2. General traffic group control algorithms: in this minor assignment, these group
control algorithms will not be discussed in any detail it thus beyond this
assignment scope. However, these algorithms work under any mix of traffic
patterns (incoming; outgoing; inter-floor).
2. Core size problem in high rise building:
The core space which include both the lift shaft space and lobby space, from
engineering point view, is a cost for the building developer, because of the loss
of net area within the building. However, one of the challenges for the lift and
architecture designer, is to find the optimum number of lifts required for the
building. However, the following parameters should be minimised in an optimal
design in the following order of importance:
1. Number of lifts.
2. Lift speed.
3. Lift capacity.
However, double-deck lifts provide a solution to core size issue by placing two
conventional single deck lifts one on top of the other and attaching them rigidly.
And, serve odd and even floor. Whereas, the TWIN lift, introduced by
Thyssenkrupp, but the original idea dates back to 1931 [16] is a developed
version of double-deck lifts. But, in twin lifts, two cars can move independently
in the same shaft. Thus, less power consumption in off-peak time.
Figure 7
Figure 8

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Figure 9
2.1. Conventional design methods, calculate the round trip time, see Figure 10, either
by carry out analytical equations such as those in [1],[17],[22], [23], [24], [25], [26],
[27] and [28], or by the use of the Monte Carlo simulation method as in [29] and
appendix (ii). these methods use a mathematical formula which can be carried out
by hand, using a program, a spreadsheet or a simulation program under incoming
traffic conditions, with some assumptions in the derivation of the round-trip time
[1] as follow:
(1) Equal floor population.
(2) Rated speed attend for a single floor journey:
Figures 11, 12 and 13 illustrate the three scenarios for speed, acceleration and
jerk, round trip-time equation depends heavily on the system kinematics.
(3) Equal floor height.
(4) Ideal system.
(5) Various lost times are negligible.
But, the drawback in this classical method is, if any of the 5 assumptions are
not satisfied, the calculation become more complicated as shown in appendix(i).

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Figure 10,
Figure 11, Plot of jerk, acceleration, velocity and displacement for the case of rated speed
attained.

9
Figure 12, One, two, three and four floor journeys with the top speed attained in all of them
Figure 13, The seven parts of the journey in which the top speed is attained.

10
Figure 14, Plot of jerk, acceleration, velocity and displacement for the case of rated speed not
attained and rated acceleration attained.
Figure 15, One, two, three and four floor journeys for the case of top speed not attained in
one and two floor journeys

11
Figure 16, Plot of jerk, acceleration, velocity and displacement for the case of rated speed not
attained and rated acceleration not attained
The two defining lift design criteria are:
(1) The quantity of Service requirements, which is the lift system handling
capacity.
(2) The quality of Service requirement, which is the interval.
However, using the mathematical formula, could results four possible cases, as
shown in detail in Table 1 below [21].
Table 1

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In [19], the HARint plane, see Figure 11, this methodology offers the lift system
designer to comply with the user requirements of arrival rate and target interval
Furthermore, this method offers the designer a graphical method to visualise the
optimality of a design. Appendix (ii), illustrate user manual for system by using
MATLAB, an example solved using Monte Carlo simulation and HARint plane for
the optimal lift design, solved problem on the HARint plane (inequal floor population
and inequal floor height the lifts system in building using both hand and MCS using
MATLAB to evaluate.
Figure 17
However, in LP2, LP3 AND LP4 self-assessment examples and in Example 3.1, in
[17], the spreadsheet used to evaluate the round-trip are limited to the previous
mentioned assumption. Both Strakosch method and Barney and Dos Santos used to
calculate the round-trip time, however, in Strakosch method does not statistically
evaluate the highest reversal floor and instead uses the top terminal floor as the
reversal floor. Whereas, in Barney and Dos Santos, followed Strakosch's work, but
including principles from the prior works reported by Lee Gray [18].

13
2.2. The second model, which is beyond the scope of this assignment, is based on a
discrete digital simulation of the movement of lifts in a building and the
passenger dynamics. [17]

14
REFERENCES
[1] G.C. Barney, “Elevator Traffic Handbook: Theory and Practice”, Taylor & Francis,
ISBN 0-415-27476-1, 2003.
[2] Bruce Powell, “Important issues in up peak traffic handling”, in Elevator Technology 4,
International Association of Elevator Engineers, Proceedings of Elevcon ’92, May 1992,
Amsterdam, The Netherlands, pp 207-218.
[3] W. L. Chan and Albert T. P. So, “Dynamic zoning for intelligent supervisory control”,
International Journal of Elevator Engineering, Volume 1, 1996, pp 47-59.
[4] W. L. Chan and Albert T. P. So, “Dynamic zoning in elevator traffic control”, in Elevator
Technology 6, International Association of Elevator Engineers, Proceedings of Elevcon ’95,
March 1995, Hong Kong, pp 133-140.
[5] Zhonghu Li, Zongyuan Mao and Jianping Wu, “Research on Dynamic Zoning of
Elevator Traffic Based on Artificial Immune Algorithm”, 8
th
International Conference on
Control, Automation, Robotics and Vision”, Kumming, China, 6th 9th December 2004.
[6] Albert T.P. So, J.K.L. Yu, “Intelligent supervisory control for lifts: dynamic zoning”,
Building Services Engineering Research & Technology, Volume 22, Issue 1, 2001, pp. 14-
33.
[7] Suying Yang, Jianzhe Tai, Cheng Shao, “Dynamic partition of elevator group control
system with destination floor guidance in up-peak traffic”, Journal of Computers, Volume
4, Number 1, January 2009, pp 45-52.
[8] Richard Peters, “Understanding the benefits and limitations of destination dispatch”, in
Elevator Technology 16, The International Association of Elevator Engineers, the
proceedings of Elevcon 2006, June 2006, Helsinki, Finland, pp 258-269.
[9] Rory Smith, Richard Peters, “ETD algorithm with destination dispatch and booster
options”, in Elevator Technology 12, International Association of Elevator Engineers,
Proceedings of Elevcon 2002, Milan, Italy, 2002, pp 247-257.
[10] Schroeder, J., “Elevatoring calculation, probable stops and reversal floor, “M10”
destination halls calls + instant call assignments”, in Elevator Technology 3, Proceedings of
Elevcon ’90, International Association of Elevator Engineers, 1990, pp 199-204.
[11] Janne Sorsa, Henri Hakonen, Marja-Liisa Siikonen, “Elevator selection with
destination control system”, in Elevator Technology 15, International Association of
Elevator Engineers, Proceedings of Elevcon 2005, Peking, China.
[12] Jorg Lauener, “Traffic performance of elevators with destination control”, Elevator
World, September 2007, pp 86-94.
[13] Microsoft cloud, (Sep 24, 2018) thyssenkrupp lays the foundation for intelligent
buildings. YouTube [online]. Available from :
https://www.youtube.com/watch?v=78yc36A58uQ. [Accessed 15 November 2019].
[14] Manuel Pijorr, Schindler Elevators, ( 22 May 2019). Digital Transformation in the
Elevator Industry – Moving from Physical Testing to Simulation. Mathwork [online]
available from: https://uk.mathworks.com/videos/digital-transformation-in-the-elevator-
industry-moving-from-physical-testing-to-simulation-1562094171320.html?s_tid=srchtitle.
[Accessed 15 November 2019].
[15] Anjasomc, (Nov 30, 2011) TWIN elevator system from ThyssenKrupp Elevator.
YouTube [online]. Available from:

15
https://www.youtube.com/watch?v=DMfqwhj_S3U&t=32s.[Accessed 15 November 2019].
[16] ThyssenKrupp, TWIN® Elevator [online]. Available from: https://www.thyssenkrupp-
india.com/media/documents/brochure/elevator/elevator_141030-twin_brochure.pdf.
[Accessed 15 November 2019].
[17] CIBSE, “CIBSE Guide D: Transportation systems in buildings”, published by the
Chartered Institute of Building Services Engineers, Fourth Edition, 2010.
[18] G.C. Barney and Richard Peters, ‘’ The Evolution of Lift Traffic Design from Human
to Expert System’’ , 9TH SYMPOSIUM ON LIFT & ESCALATOR TECHNOLOGIES,
September 2018
[19] Lutfi Al-Sharif, Osama F. Abdel Aal, Mohammad A. Abuzayyad, Ahmad M. Abu
Alqumsan, ‘’ Converting the User Requirements into an Elevator Traffic Design: The
HARint Space ‘’, 3rd Symposium on Lift and Escalator Technologies
[20] Lutfi Al-Sharif, Mohamed Hussein, Moh’d Malak, Daoud Tuffaha, ‘’ The Use of
Numerical Methods to Evaluate the Performance of Up Peak Group Control Algorithms’’,
Conference Paper · July 2014
[21] Lufti Al-sharif, The HARint Plane, a methology of systematic traffic design,
Mechatronics Engineering Department, The University of Jordan, 2016
[22] N.R. Roschier, M.J., Kaakinen, "New formulae for elevator round trip time
calculations", Elevator World supplement, 1978.
[23] Al-Sharif L. The effect of multiple entrances on the elevator round trip time under up-
peak traffic. Mathematical and Computer Modelling 2010; 52(3-4): 545-555.
[24] Peters R. Lift traffic analysis: Formulae for the general case. Building Services
Engineering Research and Technology 1990; 11(2): 65-67.
[25] R. D. Peters, “The theory and practice of general analysis lift calculations”, Proceedings
of the 4th International Conference on Elevator Technologies (Elevcon ’92), Amsterdam,
May 1992.
[26] R. D. Peters, “Vertical Transportation Planning in Buildings”, Ph.D. Thesis, Brunel
University, Department of Electrical Engineering, February 1998.
[27] Al-Sharif L and Abu Alqumsan A M. Stepwise derivation and verification of a
universal elevator round trip time formula for general traffic conditions. Building Services
Engineering Research and Technology 2015; 36(3): 311-330. doi:
10.1177/0143624414542111.
[28] Al-Sharif L, Abu Alqumsan A M and Khaleel R. Derivation of a Universal Elevator
Round Trip Time Formula under Incoming Traffic with Stepwise Verification. Building
Services Engineering Research and Technology 2014; 35(2): 198–213. doi
0143624413481685.
[29] Al-Sharif L, Dahyat H and Al-Kurdi L. The use of Monte Carlo Simulation in the
calculation of the elevator round trip time under up-peak conditions. Building Services
Engineering Research and Technology 2012; 33(3): 319–338.
doi:10.1177/0143624411414837.

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Nomenclature
• a the rated acceleration in m/s
• AR% the passenger arrivals expressed as a percentage of the building
population arriving in the busiest five minutes
• CC the rated car capacity in persons
• CL% the percentage car loading
• df the height of a floor in m
• H is the highest reversal floor in a round trip (where floors are numbered 0,
1, 2....N)
• HC% the handling capacity expressed as a percentage of the building
population that can be transported in five minutes
• HC%i the initial value of the handling capacity at the start of the iteration
• HC%f the final value of the handling capacity at the end of the iteration
• inttar the target value of the interval in s
• intact i the initial value of actual interval at the start of the iteration in s
• intact f the final value of actual interval at the end of the iteration in s
• j the rated jerk in m/s
3
• λ is the passenger arrival rate in passengers per second
• L the number of the elevators in the group
• N the total number of floors above the main entrance
• P is the number of passengers transported in one round trip
• P5 min is the number of passengers arriving for service in five minutes

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• Pact i the initial value of actual number of passengers at the start of the
iteration
• Pact f the final value of actual number of passengers at the end of the iteration
• ΔPmin is the smallest change in the value of P that will cause the iterations
to terminate
• S is the probable number of stops in a round trip
• τ the round trip time in s
• τi the initial value of the round trip time at the start of the iteration in s
• τ f the final value of the round trip time at the end of the iteration in s
• tdc the door closing time in s
• tdo the door opening time in s
• tf the time taken to complete a one floor journey in s
• tpi the passenger boarding time in s
• tpo the passenger alighting time in s
• tsd is the motor start delay in s
• tao is the door advance opening time in s (where the door starts opening
before the car comes to a complete standstill)
• tv is the time taken to traverse a floor at rated speed in s (equal to
d f
) v the
rated speed in m/s

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APPENDIX (i)
Numerical Example
This example illustrates step by step solution, using Monte Carlo simulation to calculate the
round-trip time. However, it also shows how to verify the kinematic of the lift system and how
to use probability density function (pdf) and cumulative distribution function (cdf) to evaluate
the incoming from multi-entrance building for the arrival percentages.
In the table shown below, assume that the target interval is (30 s), arrival rate equal to 6% and
incoming traffic. Lift parameters are as follow:
a. Number of floors above ground is 8 floors.
b. One ground floor and three basement car park floors.
c. Rated speed, v, is 2.5 m·s
-1
d. Rated acceleration, a, is 1 m·s .
e. Rated jerk, j, is1m·s
f. Passenger transfer time out of the car is 1.2 s.
g. assengertransfertimeintothecaris1.2s.
h. Door opening time is 2s
i. Door closing time is 3 s.
j. Total building population of 1000 persons.
k. Unequal floor heights and unequal floor populations.
Type of floor Floor Population Arrival
percentage
Floor Height
(m)
Occupant floors L8 50 N/A N/A
L7 50 N/A 3.2
L6 100 N/A 3.2
L5 100 N/A 4
L4 150 N/A 4
L3 150 N/A 4
L2 200 N/A 5
L1 200 N/A 5
Entrance/exit
floors
GROUND N/A 70% 8
B1 N/A 15% 3
B2 N/A 10% 3
B3 5% 3
Solution:

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As the building contain 4 entrances, and from the table above, firstly, calculate the arrival
percentages from the four entrances as fellow:
Parr(G) 0.70
Parr(B1) 0.15
Parr(B2) 0.10
Parr(B3) 0.05
Now the cumulative distribution function (cdf), can be represents as follow:
Arrival
Floor
x = Rand()
B3 0 ≤ 𝑥 ≤ 0.05
B3 0.05 ≤ 𝑥 ≤ 0.15
B3 0.015 ≤ 𝑥 ≤ 0.30
B3 0.30 ≤ 𝑥 ≤ 1
This arrival percentage represents the probability density function (pdf) and It can
be converted into a cumulative distribution function (cdf) as follow, as it is discrete
random variable. By draw the impulses of the probability of the passenger arrival
the results are as shown:
Parr(B3)= 0.05
Parr(B2)= 0.05+0.10=0.15
Parr(B1)= 0.05+0.10+0.15=0.30
Parr(G)= 0.05+0.10+0.15+0.70=1
Parr(G1)= 0.05+0.10+0.15+0.70=1

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Also,
U1
U
0.20
U2
U
0.20
U3
U
0.15
U4
U
0.15
U5
U
0.10
U6
U
0.10
U17
U
0.05
U8
U
0.05
by using PDF and CDF, an origin floor for each passenger can be generated based a random
number, x, that ranges from 0 to 1. Depending on the value of x, one of the four floors can be
selected.
U1
U
0.20
U2
U
0.20+0.20=0.40
U3
U
0.20+0.20+0.15=0.55
U4
U
0.20+0.20+0.15+0.15=0.70
U5
U
0.20+0.20+0.15+0.15+0.10=0.80
U6
U
0.20+0.20+0.15+0.15+0.10+0.10=0.90
U17
U
0.20+0.20+0.15+0.15+0.10+0.10+0.05=0.95
U8
U
0.20+0.20+0.15+0.15+0.10+0.10+0.05+0.05=1
Then, cdf can be expressed in a tabular format
Destination Floor x = Rand ()

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L1 0 ≤ x ≤ 0.20
L2 0.20 ≤ x ≤ 0.40
L3 0.40 ≤ x ≤ 0.55
L4 0.55≤ x ≤ 0.70
L5 0.70 ≤ x ≤0.80
L6 0.80 ≤ x ≤ 0.90
L7 0.90 ≤ x ≤ 0.95
L8 0.95 ≤ x ≤1
By using the following equation, number of passengers boarding the elevator in each round
trip. This can be found from the arrival rate (AR%) as a percentage of the building population
and the building population.
Six passengers, then twelve random numbers are required (in order to generate six random
origin floors and six random destinations). Now It is now possible to generate the origins and
destinations for these six passengers.
B2→B1→G→1→2→8→B2
Thus, the following kinematic time components must be evaluated, the distance to be
covered is 3 m. In order for the top speed to be attained, the journey must be at least
8.75 m

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Moreover, at a distance of 3 m the top acceleration is attained as 3 m is more than the required
distance of 2 m
where the journey distance is more than 2 m but shorter than 8.75 m, the rated acceleration is
attained, but the rated speed is not attained
tB2→B1, tB1→G, tG→1, t1→2, t2→8 and t8→B2.

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Thus, the total kinematic time is the sum of all the components above:
The passenger boarding and alighting time for six passengers can be simply
calculated as shown below:
The total number of stops are six (five caused by passenger destinations and one when returning
back to the start floor). The door opening and closing times for six stops can be simply
calculated as shown below:
Adding up all the three items gives the value of the round-trip time for this scenario:
By repeating a large number of scenarios, finding the round-trip time for each scenario
and then taking the average value of the round-trip time for all of these scenarios, the
correct value of the round-trip time can be found.

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APPENDIX (ii)
Modern Elevator Traffic Design User Manual
1. System Overview
2. System Requirements
3. System Components
CONTENT

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1. System Overview
The Modern Elevator Traffic Design is a graphical user interface for designing lift traffic
system. The system is built in MATLAB® 2018b by using Monte Carlo simulation and
HARint plane. following sections will explain in detail how to use this system.

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Figure 1 graphical user interface
This manual contains three main sections and sub sections. The second section explains about
the system requirements while the third section has a detailed function of each services used.
2. System Requirements
The system has the following requirements:

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PC Requirements As this system is developed on MATLAB®
2019b should be compatible with MATLAB
requirements mentioned in the following link:
https://uk.mathworks.com/support/sysreq.html
System Requirements The following structure of MATLAB files
should also available:
Some of the files are not compulsory to run
the GUI of the system but can be used for
other functions required by the engineer like
functions of image resizing and processing or
can be used to direct test using (*.m)
procedures outside the main GUI.
The first step is to double click on

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3. System Components
The system has the following components:
System Services Description
In this window, the system will
ask you to enter the lift
specification as shown
This window, system will ask
you to enter passenger
specification. The total
percentage of incoming,
outgoing, inter-entrance and
inter-floor traffic percentage
should equal to 100%.

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User have the choice, either thick
on standard or custom. If
standard, the system will choice
the correct speed. Whereas,
custom, the user decides which
speed the lift.
From building specification, user
must enter building parameters
as shown and number of trails of
MCS. Moreover, the system
gives the choice for both inequal
building population and inequal
floors height.
user should decide Run, check or
click example. On click Run the
system will show the result if the
inputs are correct.

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On click Run, the system will
estimate the results as shown.
Also, system illustrate the
HARint plane and how it
gravitate the results toward the
optimal design.

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The design of vertical transportation systems

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