The appraisal of lift passenger demand in modern office buildings

Dr. Richard Peters1, Mr Rory Smith2 and Mrs Elizabeth Evans1

Peters Research Ltd.1
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ThyssenKrupp Elevator, Inc.2

This paper was published by the Building Services Engineering Research and Technology Journal Volume 32 No 2 (2011). For copyright reasons, this version does not include changes made in response to the BSERT referees’ comments. This web version © Peters Research Ltd 2011.

Abstract

The number of passengers wanting to use lifts to travel to and from the lobby and between floors in a building has a significant effect on the quality of lift service experienced by each passenger.  The traditional assumptions of lift passenger demand in office buildings are compared to measurements taken in modern buildings.  The differences between traditional and modern patterns of passenger demand in office buildings are discussed.  The significance of these differences on lift system design is explored.


Introduction

The quantity of passengers to be transported by a lift system is a primary consideration in lift system design.

Our research indicates that passenger demand in modern office buildings is significantly different to the assumptions formed many decades ago, but still applied to most modern designs.

The number and type of lifts required to provide a proper and efficient lift service may need to be revised based on these findings.  These changes in lift system design have economic and environmental consequences that are favorable.


Historical representations of passenger demand

A plot of passenger demand depicts the level of passenger traffic in a group of lifts over a period of time.  Figure 1 shows estimated passenger demand for an office building over the working day with a population of 1000 people.  This has been generated by applying the example of office passenger demand presented by Strakosch in his book, The Vertical Transportation Handbook [1].  In this representation of passenger demand, passengers travelling up the building are shown in the top section of the graph, with passengers travelling down in the lower section.



Figure 1   Passenger demand based on presentation by Strakosch


Figure 2 plots passenger demand based on a similar pattern of office demand developed by Barney, and presented in CIBSE Guide D [2], Lift Traffic Analysis Design and Control [3] and The Elevator Traffic Handbook [4].  The patterns of passenger demand presented by Strakosch and Barney are very similar.  They have a pronounced up peak in the morning, a pronounced down peak in the evening, two small lunchtime up-peaks, and two small lunchtime down-peaks.  Additionally, periods of balanced two-way traffic can be seen.



Figure 2   Passenger demand based on presentation by Barney


The basis of these presentations is believed to be data acquired at a single building in the USA in the early 1960’s.  Many, including the authors of this paper, believe this building and its pattern of traffic demand to be typical of major city office buildings during this period.

It was generally believed that the most demanding traffic type was the morning up peak.  This belief was reinforced by research conducted by Barney that showed that lifts have between 20% and 60% more capacity during non up-peak conditions [4].

It has been assumed by many in the lift industry that most office buildings had a pattern of passenger demand similar to those in Figures 1 and 2.

Many papers have been written about how lift dispatchers should handle the different types of traffic apparent during the working day: up peak, down peak, lunch and balanced two way [5].

Many additional papers have been written about methods to either predict or detect the type of traffic that existed so that the appropriate dispatching algorithm could be applied [6].

Anyone who has visited major cities over the last 40 years will attest to the fact that many things have changed.  One may reasonably question how applicable a passenger demand pattern that existed over forty years ago is to a present day building.


Modern buildings

How people use lifts and the traffic patterns that their use generates has changed since 1923, when Basset Jones published formulae for the expected number of stops a car will make during a round trip [4].  Summarising the results of a series of peak time traffic surveys carried out between 1993 and 1997 Peters concluded, “Morning traffic peaks are less marked in buildings than they were when traditional up peak design criteria were formulated. In work-related buildings occupied during the day, the busiest period appears to be over the lunch period” [7].  In 2002, Bruce Powell, discussing modern office buildings states “two-way traffic at noontime is often a more severe test of elevators than up-peak” [8].

In 2000 Siikonen presented a traffic pattern that represents traffic measured in a modern installation [9].  Siikonen presented data as a stacked area graph, but for consistency with Figures 1 and 2, the same data is presented in Figure 3 showing incoming and outgoing traffic separately.

This pattern is quite different from the traffic pattern presented by Barney and Strakosch.  Siikonen shows a lunch up peak that is the same size as the morning up peak.  Additionally, the down peak at lunch is more intense than the evening down peak.  Both the Barney and Strakosch lunch periods show a down peak followed by an up peak, which is followed by a smaller down and up peak.  These double peaks do not occur in the Siikonen pattern.  These observations raise a question, are the differences in the patterns due to the unique nature of the building studied by Siikonen or have traffic patterns changed over the years?



Figure 3   Passenger demand based on presentation by Siikonen


The traffic pattern presented by Siikonen could be building specific, or it could represent a basic change in traffic patterns.

In order to better understand modern lift traffic, data was collected at a number of office buildings in different parts of the world including Europe, North America and the United Arab Emirates.

In most cases data was collected by manual count.  However, in one building, data from three groups of lifts in a corporate headquarters building was gathered electronically.


Manual counts

Figure 4 shows the results of lift traffic surveys for seven separate groups of lifts [10].  The surveys were undertaken applying a methodology defined by Peters and Evans [11].  The passenger demand is normalised against observed population to allow results to be compared between buildings.  The observed population is the maximum occupancy of the building on the day of the survey, and is often significantly lower than the population reported by building management.  



Figure 4   Passenger demand based on manual traffic surveys


Automatic counts

It is difficult to count automatically the number of passengers using lifts with conventional control systems which have up and down call buttons on the landings [2], as they only count calls, and there is often more than one person behind a call.  With a destination control system each passenger registers which floor they want to travel to on the landing.  A destination control system based on the ETD algorithm [12] was used to log the operation of the lifts including every destination call.  The logged data was replayed in the Elevate simulation program mapping destination calls to people, resulting in an estimate of passenger demand [13].

Figures 5 to 7 record the estimated passenger demand for the three groups or passenger lifts in a corporate headquarters building in the USA. 



Figure 5 Low rise passenger demand



Figure 6 Mid rise passenger demand



Figure 7 High rise passenger demand


Plotting data for a whole week demonstrates a high level of consistency in passenger demand.  Each group has a recognisable passenger demand pattern or “signature”.



Figure 8 Low rise passenger demand signature



Figure 9 Mid rise passenger demand signature



Figure 10 High rise passenger demand signature


Up peak traffic

In modern buildings there is often a significant amount of outgoing traffic during the morning incoming up peak.  A major contributor to this is people travelling to the main lobby or to a staff restaurant to purchase food and drink. A common practice is for a person to arrive for the first time at their desk after taking the lift up as they want to be seen as having arrived by their superiors and co-workers.  They then take the lift back down, make their purchases and finally refreshments in hand, return to their workstation by taking the lift up.

This process involves two incoming and one outgoing trip.

The trend to ban smoking in public and work places has also been noted by building managers to increase the outgoing demand during the morning incoming up peak period.

On average, the mix of traffic in modern buildings during the morning up peak was found to be approximately 85% incoming, 10% outgoing and 5% interfloor.


Lunch peak traffic

Siikonen explains part of the differences from Barney and Strakosch’s classical representations by stating “Flexible working hours creates a heavy down peak before lunch hour” [9].

The “lunch hour” is actually nearer two hours.  It is not unusual for the incoming traffic and outgoing traffic at lunchtime to be of the same order of magnitude as the incoming traffic in the morning up peak, and the outgoing traffic during the evening down peak.

Eating habits have changed over the years.  It is now less common to bring a packed lunch to eat at your desk.  All the major office buildings we surveyed were in close walking distance of a range of eat in and take away restaurants.  Many also had dedicated staff restaurants.

In 1970 US Citizens spent $6 billion on fast food [14].  By 2006 the money spent increased to $163.5 billion [15].  Even adjusting for inflation, it is obvious that Americans were eating out more often than previously.  The story in the UK is similar.  The BBC announced that UK households were spending more money on eating out than on buying food to eat at home [16].

On average, the mix of traffic in modern buildings during the busiest part of lunch was found to be 45% incoming, 45% outgoing and 10% interfloor.


Afternoon and down peak traffic

None of the groups surveyed have the sharp down peak that is seen in the Barney and Strakosch pattern.  A sharp down peak would be expected in buildings with people who have strict working times.  This is unusual in modern office buildings with professional workers.  A significant portion of office workers are working later than in previous years.

There is often sustained activity late afternoon.  The afternoon up traffic may be related to people returning to the office after visiting clients. Some traffic may also be attributed to couriers such as FedEx, DHL and UPS.  DHL began shipping Documents from San Francisco to Honolulu in 1969 [17] while FedEx started operations in 1973 [18]. The traffic generated by these couriers did not exist when the Strakosch and Barney patterns were developed.


A changing workforce

The use of computers has changed the makeup of the workforce.  Previously, office workers included many people dedicated to clerical tasks.  For example, typing pools were common but in a modern building there are virtually no typewriters.  People generate their own correspondence using email and word processing programs.  Clerical workers have been replaced by knowledge workers and by workers who have more customer interface.  Clerical workers were more likely to have fixed working hours, which contributed to higher morning and evening traffic peaks.

In recent years increased globalization has led to a change in working patterns. Business is now conducted across international time zones and working hours have become more flexible to encompass this. The traditional 9 – 5 working day has given way to a 24-hour working environment and as a result demand for lift services has also changed.


Towards new design criteria

The highest demand is seen in buildings with small populations.  This is because a low number of people represent a high percent of the building population.  These peaks are not sustained, so are manageable without designing specifically for them.

Figure 11 shows the range of total passenger demand measured in a major office with an observed population in excess of 1000 people.  Total demand includes incoming, outgoing and interfloor traffic.

In most modern office buildings, there is a greater demand at lunchtime than in the morning.  However, both morning and lunch periods need to be considered as part of the design process.  In the morning, the lifts are most crowded as people are mostly travelling in one direction and are in the car together.  At lunch time, incoming and outgoing traffic are not in the car together, which makes the cars less crowded (provided that the building is not under-lifted).  However, at lunchtime, the cars stop more often, leading to longer waiting times.



Figure 11   Passenger demand range for major office buildings


Conclusions

The pattern of passenger demand measured in our surveys closely resembles the traffic results presented by Siikonen. 
Traffic in modern office buildings is markedly different from those of the past.

While peak traffic periods still exist today, the amplitude of those peaks is not as great and the duration is longer.  Total passenger demand is normally (but not always) greater at lunchtime than it is during the morning uppeak.  Major down peaks are rarely seen.

Lift control systems should be designed to detect and manage the new patterns of passenger demand in modern buildings.

Selection of new lift systems should be based on modern as opposed to historical measurements of passenger demand.  In many instances, this will result in smaller and thus more energy efficient lifts being specified.


References

  1. Strakosch G, editor.  The Vertical Transportation Handbook. 3rd ed., Elevators and Escalators. New York: Wiley; 1998
  2. CIBSE, The Chartered Institution of Building Services Engineers. Transportation Systems in Buildings, Guide D. Norwich: CIBSE; 2005
  3. Barney, G. and Dos Santos, S. Lift Traffic Analysis Design and Control. London: Pereginus; 1977
  4. Barney, G. Elevator Traffic Handbook. London: Spon Press; 2003
  5. Bahajt Z & Bittar J, Inventor; Otis Elevator Corp,assignee. Automated selection of high traffic intensity algorithms for up-peak period.  United States patent 5,168,133.  1992 Dec. 1.
  6. Qun Z., Ming S., and Ling T.  Elevator Traffic-Flow Prediction Based on Gaussian Mixture Model. Elevator World Vol. LIV, No. 5, pp.64 -67, 2006.
  7. Peters R D. Vertical Transportation Planning in Buildings  British Library reference DX199632  (1998)
  8. Powell B.  Elevator Planning and Analysis on the Web.  Elevator World Vol. L, No. 6, pp. 73 – 77, 2002
  9. Siikonen M, Elevator Technology 10, IAEE, Israel 2000
  10. Peters Research Ltd private client reports.
  11. Peters R, Evans E Measuring and Simulating Elevator Passengers in Existing Buildings  Elevator Technology 17, Proceedings of ELEVCON 2008 (The International Association of Elevator Engineers) (2008) )
  12. Smith, R. and Peters, R.  ETD Algorithm with Destination Dispatch and Booster Options. Elevator World Vol. L, No. 6, pp. 136 – 142. 2002
  13. Peters R, and Smith R. Analysis of Elevator Performance and Passenger Demand with Destination Control.  Elevator Technology 17, IAEE, Israel 2008
  14. Wikipedia.  Fast Food.  [Cited 2007 Jul 21].  Available from: http://en.wikipedia.org/wiki/Fast_food
  15. How Stuff Works. The History of Fast Food.  [Cited 2007 Jul 21].  Available from: http://home.howstuffworks.com/fast-food3.htm
  16. BBC.  Eating Out Overtakes Home Dinning.  [Cited 2007 Jul 21]  Available from:http://news.bbc.co.uk/go/pr/fr/-2/hi/business/5263156.stm
  17. DHL company Portrait [Cited 2008 Dec 6]  Available from: http://www.dhl.com/publish/g0/en/about/history.high.html
  18. FedEx History. [Cited 2008 Dec 6]  Available from: http://about.fedex.designcdt.com/our_company/company_information/fedex_history