Richard D Peters, John Haddon
Ove Arup & Partners
This paper was presented at ELEVCON BARCELONA 1996, The International Congress on Vertical Transportation Technologies and first published in the IAEE book “Elevator Technology 7”, edited by G. C. Barney. It is reproduced with permission from The International Association of Elevator Engineers. The paper was also presented at IAEE London Lift Seminar (May 1997). This web version © Peters Research Ltd 2009.
In order to calculate the performance of a new or refurbished lift installation we need to estimate likely passenger traffic patterns. These traffic patterns are also recognised by the lifts’ traffic control system which adjusts the dispatcher algorithm accordingly. In this paper the authors summarise current, published knowledge of lift passenger traffic patterns and review these against actual survey results. Having identified a need to improve our knowledge of passenger traffic patterns, various means of collecting this data are discussed including: manual surveys, computer vision techniques, infra-red counters and analysis based on data logged by lift traffic control systems or traffic analysers.
Assessment of performance is a crucial element in lift (elevator) design. If lifts are too small, slow, or insufficient in number, passengers have to wait for excessive periods for a lift to arrive in response to landing calls. Furthermore, passengers travelling more than a few floors in under-lifted installations often endure long journey times – the result of the lifts having to stop to answer other calls at most of the intermediate floors. On the other hand, the luxury of an over-lifted building is an expensive one – floor area that could be let to tenants is lost to additional or larger lift lobbies and shafts; capital, maintenance and energy costs of the installation are higher.
The need to specify appropriate numbers of lifts, their capacity and speed, etc. has led to the study of lift traffic analysis. Lift traffic analysis allows us to assess the performance of a proposed lift installation based on estimates of building passenger traffic patterns. Lift traffic analysis techniques ranging from up peak calculations to general analytical formulae and simulation techniques are well developed and widely applied. But lift performance results from lift traffic analysis are of no better quality than the estimated passenger traffic patterns that are used in the calculations or simulations.
In operation, lift control systems adapt to changing demands based on their designers’ understanding of passenger traffic patterns. Control strategies appropriate to the current traffic pattern (e.g. up peak, down peak algorithms) can improve performance significantly.
In this paper the authors summarise current, published knowledge of lift passenger traffic patterns and compares this with survey results. Current design guidelines are questioned, and means of improving our knowledge of lift passenger traffic patterns are discussed.
In estimating prospective passenger traffic patterns, a designer might consult:
There are other sources of information, including manufacturers’ planning guides, but these tend to re-iterate the recommendations of above. Barney, dos Santos and Strakosch present example diagrams of passenger traffic in a commercial, office building. These diagrams have been re-drawn in Figures 1 and 2.
Figure 1 Typical office traffic, Barney
Figure 2 Typical office traffic, Strakosch
According to Barney and dos Santos, conventional design procedure is to determine the performance of lift systems for the morning up peak traffic situation. This is consistent with the authors’ experience from reviewing consultants’ and manufacturers’ calculations. The common approach is probably because:
CIBSE Guide D suggests the following up peak traffic flows for design purposes:
|Building Type||Arrival rate
(% in 5 minutes)
|Building Type||Arrival rate
(% in 5 minutes)
|Office (multiple tenancy)||11-15 regular, 17 prestige||Office (single tenancy)||15 regular, 17-25 prestige|
Table 1 CIBSE Guide D guidance on peak arrival rates
Strakosch places most emphasis on the incoming up peak traffic, but also proposes two-way and outgoing traffic criteria. BS 5655 Part 6 offers only up peak design criteria.
Detailed lift traffic surveys carried out by researchers, consultants and manufacturers are rarely published. One exception is A survey of passenger traffic in two office buildings published by BRE in 1974. Results are summarised in Table 2.
|Building||Traffic period||Peak 5 min % building population using lifts|
|Southbridge House||morning up peak
evening down peak
|Sanctuary Buildings||morning up peak
evening down peak
Table 2 Summary of BRE traffic survey results
The BRE survey also concluded that lunch time traffic amounts to 12% of building population in both buildings, but this includes stair traffic.
Passenger traffic surveys have been carried out on behalf of the authors at a range of buildings. Results are summarised in Figures 3 to 7 which record the traffic to and from the main terminal floor(s), except for Building E where the predominant traffic flow was inter-floor. Traffic was measured only during peak periods (normally morning, lunch and evening; morning and evening for the hotel).
Figure 3 Building A traffic survey results for single tenancy office, engineering
Figure 4 Building B traffic survey results for single tenancy office, banking/dealers (results based on nominal population of 1 person/10m2 as actual occupancy not available)
Figure 5 Building C traffic survey results for single tenancy office, general
Figure 6 Building D traffic survey results for prestigious traditional hotel
Figure 7 Building E traffic survey results for major high rise hospital (results not shown as % as only one of two passenger lift banks available for survey)
The traffic survey results suggest that the 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. Lunch traffic is a combination of up and down peak traffic to the main terminals, but often includes an element of inter-floor traffic. This inter-floor traffic is especially significant in buildings with restaurants, meeting rooms, etc. It can be shown that, if the same total handling capacity is assumed, people wait longer for a lift at lunch time than they do during a morning up peak. This is because the combination of passengers travelling up and down the building results in more stops per round trip. Consequently, the authors suggest that future designcriteria for traffic analysis should use the lunch peak as a primary design criterion. It would be dangerous to disregard established up peak design criteria without a wider study of building traffic flow peaks. Thus the remainder of this paper discusses means of representing and collecting traffic data so that, in due course, updated design criteria can be formulated fora wide range of buildings.
Traditionally lift traffic flows have been defined in terms of the percentage of the building population transported upwards and downwards in five minutes, as used in Figures 1-6. For more complex flows such as lunch peaks we need a more comprehensive way of describing lift traffic. Peters presented an approach in his paper on General Analysis Lift Calculations that allows us to describe traffic flow completely. Two terms are required:
μi the passenger arrival rate at floor i (defined for each floor at which passengers may arrive)
dij the probability of the destination floor of passengers from floor i being the jth floor (defined for all possible i and j)
Using these terms, a simple up peak traffic flow in an office block could be represented as in Figure 8. And a more complex traffic flow could be represented as in Figure 9.
Future design criteria should enable the designer to estimate peak traffic flows in these terms from a knowledge of the office building population, number of hotel rooms, etc. dependant on the building type. The lift performance can then be assessed analytically, or by simulation.
The are a number of alternative approaches to collecting data on lift passenger traffic patterns. Those considered by the author are discussed in the following subsections. Other and new technologies may yield alternative approaches.
In manual surveys observers count passengers in and out of the lifts. Manual surveys are normally based on one of two approaches:
Manual surveys are discussed in detail in  and . The new generation of cheap, miniature video cameras (used with a video recorder) can be used to make observation unobtrusive; the recorded video is played back off site for counting.
The survey techniques do not allow us to describe traffic flow completely as:
measures arrival rates at all floors, so provides superior data to . Overall destinations probabilities (averaged over all arrival floors) can be approximated from the count of passengers as they leave the lift. Collecting data to enable traffic to be described completely is impractical for the human observer unless traffic is light – to achieve a full data set of destination probabilities, the observer would have to track every passenger, e.g. passenger 53 entered the lift at floor 3 and alighted at floor 6; passenger 54 entered the lift at floor 4 and alighted at floor 10, etc.
Traffic analysers are linked to the lift control system, and record the time every landing and car call is made and cleared. They analyse this data and provide a range of performance results and graphs. Modern control systems incorporate similar functionality.
A range of traffic and performance measures can be determined, for example:
Traffic analysers give a good indication of a lift system’s performance, but very limited information about the actual passenger traffic flow. This is because they have no means of determining the number of people transported on each trip, e.g. a landing call at floor five and corresponding car call to floor seven could equally be a single person, or a group of people travelling together. The use of accurate weighing devices would provide a guide to passenger load. But ambiguities occur if people are loading and unloading at the same floor, e.g. five people loading and three people unloading would provide the same weight differential as two people loading.
Therefore, on its own, traffic analyser data does not give us the information we require.
Al-Sharif suggested a means of interpreting data that is available to traffic analysers. The Inverse S-P method applies conventional up peak traffic analysis formulae “backwards” to estimate the number of passengers using a lift from the number of car calls and lift movements. The Inverse S-P method is effective, yet applies only to up and to down peak traffic.
Peters reported having derived a method for extrapolating (complete) traffic flow from control systems data in. The development of this method has been halted after successful preliminary tests as further work is impractical without taking data directly from lift system controllers. Manufacturers have proved unable or unwilling to provide access to the necessary data for research purposes. The proposed method is outlined as follows:
Figure 10 records some results from the preliminary tests where control system data was collected “manually” by observation.
Figure 10 Poisson based estimate of traffic flow
Researchers have applied image processing techniques to video pictures of lift lobbies to determine the number of people waiting for the lifts. This lobby count aids the control system by enabling it to prioritise calls from busy floors.
A spin off from the lobby count system developed at Brunel University was a prototype “traffic surveyor” to count the passengers as they loaded and alighted the lifts. The system applies similar image processing techniques to the lobby count system, but compares each video frame in sequence to track people across the scene. If people join or leave the scene from the areas defined as the lift doors, they are counted as having loaded or alighted the lifts. In tests the system was found to be 80-85% accurate, errors being due mainly to a tendency to miss-track people from one image to the next.
This Brunel University research project has now concluded, so no further development is envisaged. But image processing is an active research area and improved pedestrian tracking systems are likely to be developed in the future, probably initially for security applications. In due course, we are likely to be able to purchase general purpose pedestrian tracking systems that will provide us with the basis for complete measurements of traffic flow.
Infra-red technology is widely applied, particularly in the security industry. Traffic surveys using photocells or infra-red beams were suggested in . The approach requires a minimum of two horizontal beams to count people passing through the detection field in single file. The sequence of beam states enables direction to be determined. If people are walking side by side, horizontal beams will detect only a single person. This can be overcome by mounting beams vertically – a system believed to be using this approach is installed in a London department store monitoring escalator traffic.
Initial lab and site tests suggest that, although system logic can be fooled, in practice the overall counting accuracy of infra-red counting systems is high. The infra-red detectors effectively replace observers in manual surveys, so the data collected does not describe traffic flow completely (as in 6.2 ii we can only calculate average destination probabilities). But infra-red technology is available and relatively inexpensive to implement.
Written surveys, where people record the times of lift trips on a form, have been found to be unreliable; this was confirmed from the results of a written survey at Building A (Figure 3). This is probably due to a tendency for people to record their arrival and departure times as the fixed working hours of a company.
Various security systems are applied to control access in buildings, some of which are integrated with the lift systems. Systems that use swipe cards to call the lift, or a key pad to control access to specific floors, do not yield useful traffic flow data. Where they are installed, systems that identify passengers individually as they arrive and depart lift lobbies, will enable traffic flow to be monitored completely.
In planning lift installations, some designers make allowance for the use of stairs. The author’s survey experience suggests:
In the Building C (Figure 5) survey, use of the staircase was virtually nil in spite of the lifts being heavily loaded and long passenger waiting times; the main staircase was an unattractive fire escape sited well away from the lift lobby. Figure 11 shows the associated stair usage for the BRE and Building A (Figure 3) surveys
Figure 11 Example stair usage for up and down travel
In lift traffic surveys we need to assess stair usage, otherwise generalised recommendations will be inappropriate to:
buildings where stair access is poor
If the results of traffic surveys are to be applied in the design of other buildings, it is important that traffic is recorded relative to the actual building population – plotting survey results of a partly occupied building relative to nominal building population can suggest misleadingly low traffic flows.
It is important for lift designers to have a good understanding of lift traffic flows. Most lift installations are designed on the basis that the morning up peak is the most onerous traffic condition for lifts, yet traffic surveys suggest that the lunch period is more often the busiest period.
In planning new lift installations, it would be dangerous to disregard conventional up peak design criteria completely without a wide study of other traffic flow peaks. But in many cases designs applying up peak traffic analysis are in appropriate.
A range of surveying techniques has been reviewed as a means of establishing passenger traffic flows. The authors continue to collect data, and encourage others to pulish their results so that improved design criteria can be established. Survey techniques are improving – currently the authors faviour the infra-red system as the best available technology. Improved knowledge of traffic flows will also aid control system design.
The authors would like to thank the Engineering and Physical Sciences Research Council, The Ove Arup Partnership, and the Chartered Institution of Building Services Engineers for their financial support.