{"id":1087,"date":"2020-11-17T11:51:40","date_gmt":"2020-11-17T11:51:40","guid":{"rendered":"https:\/\/wordpress.peters-research.com\/?page_id=1087"},"modified":"2020-11-17T12:12:17","modified_gmt":"2020-11-17T12:12:17","slug":"the-application-of-simulation-to-traffic-design-and-dispatcher-testing","status":"publish","type":"page","link":"https:\/\/wordpress.peters-research.com\/index.php\/papers\/the-application-of-simulation-to-traffic-design-and-dispatcher-testing\/","title":{"rendered":"The Application of Simulation to Traffic Design and Dispatcher Testing"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"1087\" class=\"elementor elementor-1087\">\n\t\t\t\t\t\t<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-2bd44fca elementor-section-full_width elementor-section-height-default elementor-section-height-default\" data-eae-slider=\"77251\" data-id=\"2bd44fca\" data-element_type=\"section\" 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class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-624c56e elementor-widget elementor-widget-heading\" data-id=\"624c56e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">The Application of Simulation to Traffic Design and Dispatcher Testing\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-550f1783 elementor-widget elementor-widget-text-editor\" data-id=\"550f1783\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Dr Richard D Peters<br \/>Peters Research Ltd.<span id=\"cloak4d5f1b2ea5117c0b84fc66f8779922b4\"><\/span><\/p><p>This paper was presented at The 3rd Lift Symposium on Lift and Escalator Technologies 2013\u00a0in Northampton.\u00a0This web version \u00a9 Peters Research Ltd 2013.<\/p><h3><br \/>Abstract.\u00a0<\/h3><p>Simulation is a popular traffic design tool, but there are many different ways in which it can be applied and the interpretation of results can be difficult. The relationship between round trip time calculations and simulation is explored, demonstrating consistency, but also highlighting why results can be very different.\u00a0 Simulation templates allow hypothetical and measured traffic patterns to be applied in the selection of lifts for new buildings, and in assessing the benefits of modernisation.\u00a0 The strengths and weaknesses of popular templates are discussed.\u00a0 Common misunderstandings are explained. Dispatcher testing can be approached in a similar way to traffic design, but success in sample traffic design simulations does not guarantee consistent performance across a range of traffic conditions and building configurations.\u00a0 A more comprehensive approach is proposed.<\/p><h3><br \/>1. Introduction<\/h3><p>Lift simulation models of varying sophistication have been written and applied since the early 1970s [1].\u00a0 The continuing improvements in computer technology and software development tools make increasing complex and comprehensive simulation models feasible.\u00a0 In the late 1990s non-proprietary simulation software for modern operating system became available, making simulation popular and available to most lift companies and consultants [2]. Lift simulation is a very powerful tool.\u00a0 However it is good practice to start all design exercises with a round trip time calculation [3].<\/p><p>With round trip calculations we model a single, average round trip.\u00a0 In simulation the whole process of passengers arriving at the landings, registering their landing calls, boarding the lifts when they arrive, registering their car calls and then alighting at their destination is modelled.\u00a0 Simulation calculates the performance for every call and every passenger.\u00a0<\/p><p>Simulation can be used to model scenarios that cannot normally be analysed with the round trip time calculations, including:<\/p><ul><li>Light (non-peak) traffic<\/li><li>Changing levels of traffic, e.g. the increasing levels of traffic as the work start time approaches in an office building<\/li><li>Mixed types of traffic, e.g. goods and passenger traffic using the same lifts<\/li><li>Lifts in the same group with different speeds and sizes.<\/li><\/ul><h3>2. Describing traffic<\/h3><p>With general analysis round trip calculations [4] we can analyse:<\/p><ul><li>mixed traffic, defining a demand as a percentage of the building population, divided into incoming, outgoing and interfloor components<\/li><li>entrance level bias to allow for car parking floors, restaurant floors and other utility floors<\/li><li>arrival rate and destination probability tables, for traffic which cannot be described in simpler terms.<\/li><\/ul><p>All these ways of describing traffic can all be applied in simulation.\u00a0 With round trip time calculations the assumption is that demand is constant; with simulation we can introduce templates which include a time element, see Figure 1.\u00a0 In this paper we will consider constant and step templates; these are theoretical templates not based on real traffic in buildings.\u00a0 Then we will consider templates derived from traffic surveys.<br \/>\u00a0<\/p><p>\u00a0<img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/TheApplicationofSimulationtoTrafficDesignandDispatcherTesting\/figure%201.jpg\" alt=\"\" \/><\/p><p><em><strong>Figure 1 Demand may be constant or vary with time<\/strong><\/em><\/p><h3>3. Constant traffic template<\/h3><p>With a constant traffic template the premise is that if a system has a handling capacity of x%, it can sustain that demand indefinitely.\u00a0 This is directly analogous with the round trip time calculation.<\/p><p><br \/>Example 1 Simulation of up peak calculation<\/p><p><br \/>Perform a round trip time calculation and simulation for the parameters given in Table 1.\u00a0 Run the simulation for 30 minutes ignoring the first and last five minutes to allow for start and end conditions.\u00a0 Apply a group collective algorithm with up-peak mode.<\/p><p><br \/><em><strong>Table 1 Up peak calculation and simulation parameters<\/strong><\/em><\/p><p>\u00a0<img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/TheApplicationofSimulationtoTrafficDesignandDispatcherTesting\/capture1.jpg\" alt=\"\" \/><\/p><p>Results are given in Table 2; in this case there is a close correlation between the up-peak calculation and simulation.<\/p><p><em><strong>Table 2 Result for comparison between round trip times and simulation<\/strong><\/em><\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/TheApplicationofSimulationtoTrafficDesignandDispatcherTesting\/table%202.jpg\" alt=\"\" \/><\/p><p><em><strong>Example 2 Simulation demonstrating saturation<\/strong><\/em><\/p><p><br \/>A lift group saturates when the demand exceeds the handling capacity.\u00a0 As the lifts cannot cope with the traffic, the longer the simulation runs, the longer the passenger waiting times become.\u00a0 Increasing queue lengths develop as the simulation progresses.<\/p><p><br \/>To demonstrate saturation, repeat the simulation in Example 1 with the demand increased from 14% to 15% and then to 16% of the building population requiring transportation in five minutes.\u00a0 Results are given in Table 3.<\/p><p><strong><em>Table 3 Result for comparison between round trip times and simulation<\/em><\/strong><\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/TheApplicationofSimulationtoTrafficDesignandDispatcherTesting\/table3.jpg\" alt=\"\" \/><\/p><p>Notice that with increasing demand the interval remains relatively stable.\u00a0 Up-peak interval is the time between lift departures from the entrance floor.\u00a0 For all three results the lifts are departing full from the ground floor.\u00a0 When the demand increases, there a queue is forming.\u00a0 So, passengers have to wait more than one interval before they can board a lift.\u00a0 This is reflected in the rapidly increasing average waiting times and queue length.<\/p><h4>Avoiding confusing simulation results with the constant traffic template<\/h4><p>Round trip time calculations for office buildings are often carried out to establish the maximum handling capacity of a system.\u00a0 So, if you run a simulation based on your round trip time calculation it is likely that the simulation will be near or at the saturation point.\u00a0 If the simulation saturates, then results become unstable; a solution which was acceptable when analysed with a round trip time with simulation can present long queues and unacceptable waiting times.\u00a0 As the simulation is unstable, small changes in any parameter can have a large and sometimes counter-intuitive effect on results.<\/p><p>When comparing round trip time calculations with simulations, it is important to note:<\/p><ul><li>often designers using round trip time calculation do not consider door dwell times<\/li><li>round trip time calculations are based on averages and may be based on the assumption a car is loaded with say 9.9 persons; a simulation with multiple runs also yields an average, but in each simulation the maximum car load is an integer number of persons<\/li><li>unless a round trip time inefficiency is used, round trip time calculations assume an ideal system with, for example, no bunching, no door re-openings or other \u201creal life\u201d delays.<\/li><\/ul><h3><br \/>4. The step profile<\/h3><p>This template shown in Figure 2 starts with a low demand and increases constantly or, in increments of 1% every period.\u00a0 The demand can be pure up-peak, or any combination of mixed traffic.\u00a0 The premise of this approach is that the system\u2019s performance is tested across a range of traffic intensities.\u00a0<br \/>\u00a0<\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/TheApplicationofSimulationtoTrafficDesignandDispatcherTesting\/figure%202.png\" alt=\"\" \/><\/p><p><em><strong>Figure 2\u00a0 Passenger demand for step profile increasing by 1% every period\u00a0<\/strong><\/em><\/p><p>This presentation is useful as it highlights to the customer that the waiting time, loading, and other parameters are dependent on demand.\u00a0 A system that manages 12% of the design population in 5 minutes may be sufficient in most buildings.\u00a0 However, if it can transport a greater demand without saturating, it is more likely to manage, for example, if the building population exceeds the design population.\u00a0 The simulation should continue to at least 1% beyond the design value for passenger demand.<\/p><p>\u00a0<br \/>Example 3 Application of step profile<\/p><p><br \/>Repeat Example 2 with a step profile.\u00a0\u00a0 Begin at 1% demand increasing traffic at 1% increments every 30 minutes up to a maximum of 16%.\u00a0 Results are given in Table 4.<\/p><p><br \/><em><strong>Table 4 Quality of service results for increasing demand<\/strong><\/em><\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/TheApplicationofSimulationtoTrafficDesignandDispatcherTesting\/table4.jpg\" alt=\"\" \/>\u00a0<\/p><p>When the demand exceeds the handling capacity (15%), the system becomes unstable.\u00a0 Up to this point the table provides a good indicator of how the system will perform across a range of traffic intensities.\u00a0 Note in the close correlation between the waiting times calculated with the constant traffic template and the step profile when the demand is 14%, see Table 5.<\/p><p><br \/><em><strong>Table 5 Comparison of constant traffic template and step profile template.<\/strong><\/em><br \/>\u00a0<img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/TheApplicationofSimulationtoTrafficDesignandDispatcherTesting\/table%205.jpg\" alt=\"\" \/><\/p><h3><br \/>5. Simulation templates derived from traffic survey<\/h3><p>The templates presented in previous sections are not intended to represent actual passenger demand in buildings; they are tools to assist designers establish an appropriate design. The most authoritative position when predicting how a proposed lift installation will perform is to design applying evidence based research.\u00a0 Templates have been proposed which are intended to represent real traffic in actual buildings [5], [6], [1], and Powell for [2].\u00a0 New design templates for offices were developed [3] to reflect the traffic in modern office buildings, see Figure 3.\u00a0\u00a0 Each template represents one hour in twelve 5-minute periods.<\/p><p>\u00a0<img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/TheApplicationofSimulationtoTrafficDesignandDispatcherTesting\/figure3.jpg\" alt=\"\" \/><\/p><p><em><strong>Figure 3 CIBSE modern office up-peak and lunch-peak traffic templates<\/strong><\/em><\/p><p><em><strong>Example 4 Application of modern office templates<\/strong><\/em><\/p><p><br \/>Repeat Example 1 in simulation applying the CIBSE modern office templates.\u00a0 Results for simulations based on the up-peak template are given in Figure 4 and Figure 5.\u00a0 Test against target requirements for prestigious city office [3].\u00a0 The up-peak requirements are for average waiting time during the worst five minutes not to exceed 20 seconds; and for the average transit time not to exceed 80 seconds.\u00a0 These requirements are both met.<\/p><p>\u00a0<img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/TheApplicationofSimulationtoTrafficDesignandDispatcherTesting\/figure%204.png\" alt=\"\" \/><\/p><p><em><strong>Figure 4 Average waiting time (solid) and time to destination (dotted) applying CIBSE modern office up peak template<\/strong><\/em><br \/>\u00a0<\/p><p>\u00a0<img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/TheApplicationofSimulationtoTrafficDesignandDispatcherTesting\/figure%205.png\" alt=\"\" \/><\/p><p><em><strong>Figure 5 Average (solid) and maximum (dotted) car loading on departure from home floor applying CIBSE modern office up-peak template<\/strong><\/em><\/p><p><br \/>The up-peak loading requirements are for the capacity factor by area not to exceed 80%.\u00a0 This is met.\u00a0<br \/>Results for the simulations based on the lunch-peak template are given in Figure 6.<br \/>\u00a0<\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/TheApplicationofSimulationtoTrafficDesignandDispatcherTesting\/figure%206.png\" alt=\"\" \/><\/p><p><em><strong>Figure 6 Average waiting time (solid) and time to destination (dotted) applying CIBSE modern office lunch peak template<\/strong><\/em><\/p><p><br \/>The lunch-peak requirements are for average waiting time during the worst five minutes not to exceed 30 seconds; and for the average transit time not to exceed 100 seconds.\u00a0 These requirements are both met.\u00a0 The lunch-peak loading requirements are for the capacity factor by area not to exceed 80%.\u00a0 This is met easily; loading during lunch is less critical as people are not all in the car at the same time; some in the car for the up trip, others for the down trip.\u00a0 Waiting times are typically longer as lifts stop for calls during both the up and down trips.<\/p><h3><br \/>6. Interval and waiting time<\/h3><p>When clients and designers familiar with round trip time calculations first apply simulation, they sometime continue to use interval as a quality of service measure.\u00a0 This sometimes leads to confusion as interval does not always reflect quality of service.<\/p><h4>Interval in an ideal system<\/h4><p>Consider a lift system with an interval of 30 seconds.\u00a0 A lift departs the main entrance floor every 30 seconds as indicated in Figure 7.\u00a0\u00a0 If people are arriving at a constant rate, the first passenger shown on the time line just misses the lift.\u00a0 He or she has to wait 30 seconds.\u00a0 The final passenger shown on the time line just catches the lift, so waits 0 seconds.\u00a0 The average passengers wait 15 seconds.\u00a0 So, in a perfect system the average waiting time is 15 seconds, or half the interval.<\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/TheApplicationofSimulationtoTrafficDesignandDispatcherTesting\/figure7.jpg\" alt=\"\" \/>\u00a0<\/p><p><em><strong>Figure 7 Comparing interval and waiting time<\/strong><\/em><\/p><h4>Interval across a range of traffic intensities<\/h4><p>The scenario characterised in Figure 7 reflects our understanding of round trip time calculations.\u00a0 In the real word, and with more sophisticated simulation models, the relationship is not this simple.\u00a0 One way of investigating this is with a step profile.\u00a0 In Example 3 the up-peak demand increased by 1% every 30 minutes.\u00a0 Figure 8 shows the corresponding interval with increasing traffic demand.<br \/>\u00a0<\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/TheApplicationofSimulationtoTrafficDesignandDispatcherTesting\/figure8.jpg\" alt=\"\" \/>\u00a0<\/p><p><em><strong>Figure 8 Interval for increasing traffic demand<\/strong><\/em><\/p><p>For the same demand profile, consider the plot of waiting time (and time to destination) as given in Figure 9.<\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/TheApplicationofSimulationtoTrafficDesignandDispatcherTesting\/figure9.jpg\" alt=\"\" \/>\u00a0<\/p><p><em><strong>Figure 9 Average waiting time (solid) and time to destination (dotted) for increasing traffic demand<\/strong><\/em><\/p><p>The idealised interval to waiting time relationship we see in Figure 7 only occurs just before the simulation saturates.\u00a0 Average waiting time proves the better measure of quality of service.<\/p><h4>Other difficulties with interval<\/h4><p><strong>Non-peak traffic<\/strong>\u00a0 With low demand, the interval in simulation becomes high as cars are not being dispatched regularly from the main entrance floor; sometimes they are sitting idle, see the start of Figure 8. It is generally accepted [7] that for low traffic scenarios such as residential buildings, simulation is the better tool, and waiting time should be used in preference to interval.<\/p><p><strong>Multiple entrance floors<\/strong>\u00a0 Interval is a measure of the time between lift departures from the main entrance floor.\u00a0 With multiple entrance floors, not every lift stops at the main entrance floor on every round trip.\u00a0 This causes high intervals; again interval falls down as a measure of quality of service.<\/p><p><strong>Destination Control<\/strong>\u00a0\u00a0 With destination control passengers are allocated to a specific car, so they do not take the next car to depart.\u00a0 So, even if the interval is 20 seconds, it may be two or three intervals until the car allocated to a passenger departs.\u00a0 Some early presentations of destination control reported excellent intervals, which were potentially misleading; the interval does not correlate with quality of service with these systems.<\/p><h4>Discussion<\/h4><p>Interval is a very useful measure of quality of service in the context of round trip time calculations. In simulation it is an interesting result, but can be confusing without a clear understanding of what is being measured.\u00a0 If simulation is required, but the design criteria specified is interval, it is advisable to target an equivalent average waiting time.\u00a0 Barney suggests that the relationship is a function of loading [1] , as also demonstrated in this paper.\u00a0 Strakosch [6] suggests the relationship is approximately 60%, which is consistent with the author\u2019s simulations at a traffic levels marginally below the saturation point.\u00a0 Therefore, for example, a target interval of 30 s could be interpreted as a target average waiting time of 18 s.<\/p><h3>7. Traffic control system testing<\/h3><p>Most traffic control systems have strengths and weaknesses; the step profile is a good way of testing dispatching strategies, which do not necessarily perform consistently across a range of traffic intensities and traffic split (incoming, outgoing, interfloor).<\/p><p>Example 5 Testing traffic control system performance across a range of traffic intensities<\/p><p>Weaknesses in the management of outgoing traffic can often be observed in buildings where people are attending a large meeting or event with a fixed end time.<\/p><p>Repeat Example 3 with 100% outgoing traffic.\u00a0 Run the simulation with a group collective dispatcher with and without the application of a down peak algorithm.\u00a0<\/p><p>\u00a0<\/p><p>\u00a0<img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/TheApplicationofSimulationtoTrafficDesignandDispatcherTesting\/figure%2010.png\" alt=\"\" width=\"600\" \/><\/p><p>Figure 10 Comparison of average passenger waiting times across a range of passenger demands<\/p><p>The group collective algorithm is based on allocating the \u201cnearest car\u201d, which is a simple, but effective way of minimising system response time.\u00a0 This strategy works reasonably until demand exceeds handling capacity.\u00a0 At this point, a lack of handling capacity is the problem.\u00a0 The down peak algorithm [1] reduces the average number of stops per round trip, which reduces the round trip time and increases the handling capacity.\u00a0 The increased handling capacity results in lower waiting times.<\/p><p>Example 6 Example of traffic control system collapse in saturation<\/p><p>It is well understood that destination control boosts up-peak handling capacity.\u00a0 However some destination control installations perform poorly where the demand exceeds the boosted handling capacity.<\/p><p>This is easiest to illustrate by extending Example 3 and plotting passenger transfer (people who have loaded the lifts) with demand.\u00a0\u00a0 Figure 11 shows up-peak demand increasing to a point where it exceeds the handling capacity of a conventional system (in this case approximately 14%).\u00a0 Queues will be forming, but the system is still delivers 14% handling capacity.\u00a0 The up-peak handling capacity of the sample destination control system is greater (in this case approximately 17%).\u00a0 However when the demand exceeds the boosted handling capacity the system manages saturation poorly, and its handling capacity drops to approximately 10%.\u00a0\u00a0<\/p><p>This collapse in handling capacity has been observed in real buildings.\u00a0 It happens because the dispatcher concept does not consider the saturation scenario.\u00a0 There are a number of ways of to address this.<br \/>\u00a0<\/p><p>\u00a0<img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/TheApplicationofSimulationtoTrafficDesignandDispatcherTesting\/figure%2011.png\" alt=\"\" width=\"600\" \/><\/p><p>Figure 11 Increasing demand followed by passenger transfer until handling capacity reached, showing subsequent collapse of handling capacity in some cases<\/p><p>For comprehensive testing, the designer should consider all recognised traffic conditions (up-peak, lunch-peak, and down-peak).\u00a0 Scenarios should include multiple entrance floors and special floors such as restaurant and conference levels.<\/p><h3>8. Other considerations<\/h3><h4>Multiple runs<\/h4><p>In most cases it is best to carry out multiple (typically ten) simulation runs.\u00a0 This gives us greater sample size with which to generate results that are statically significant.<\/p><p>Multiple runs can be achieved by using different random number seeds with the same arrival rates and destination probabilities.\u00a0 The demand is the same, but passengers are arriving at slightly different times.\u00a0 It can be helpful to think of this as modelling different days of the week, Monday, Tuesday, Wednesday, etc.\u00a0 Results can then be averaged for all the simulations.\u00a0<\/p><p>Without multiple simulations, the chance element in simulation means that changing a parameter, such as speed or door operating times can sometimes lead to performance results getting worse when it would be expected for them to improve (or vice versa).\u00a0 For example, if doors times are changed to be slightly slower, in one simulation a passenger may catch a lift which they otherwise would have missed.\u00a0 This may impact results in one simulation run, but if multiple simulations are performed the advantage of the improved door times will be demonstrated.<\/p><p>The smaller the variation, the greater number of simulations will be required.\u00a0 For example, if door times are improved by 0.1s, it may be necessary to run fifty simulations to demonstrate that average waiting time is also improved, if only by a fraction of a second.<\/p><h3>9. Discussion<\/h3><p>Simulation is a powerful tool which overcomes the limitations of round trip time calculations.\u00a0 However it introduces many complexities to do with real operation, which are not captured in round trip models.\u00a0 Simulations applying a constant traffic template are useful for understanding the relationship between round trip time calculations and simulation; result correlate well if the input assumptions are consistent.\u00a0 Simulations with the step profile give us a better understanding of how lift systems perform across a range of traffic intensities.\u00a0 Simulations based on traffic surveys give us more realistic estimates of how planned lift installations will operate, and the basis for a better assessment of the value of different technologies.<\/p><p><br \/><strong>REFERENCES<\/strong><\/p><p>[1] \u00a0G. Barney, Elevator Traffic Handbook, London: Spoon Press, 2003.<br \/>[2] \u00a0R. Peters, \u201cElevate\u2122,\u201d 1998 to present. [Online]. Available: www.peters-research.com.<br \/>[3] \u00a0R. Peters, \u201cAdvanced planning techniques and computer programmes,\u201d in CIBSE Guide D:2010 Transportation systems in Buildings, London, The Chartered Institution of Building Services Engineers, 2010.<br \/>[4] \u00a0R. Peters, \u201cLift Traffic Analysis: Formulae for the General Case,\u201d Building Services Engineering Research and Technology, vol. 11, no. 2, 1990.<br \/>[5] \u00a0M.-L. Siikonen, \u201cOn Traffic Planning Methodology,\u201d in Elevator Technology 10, 2000.<br \/>[6] \u00a0G. Strakosch and R. Caporale, The Vertical Transportation Handbook, Fourth Edition, Hoboken, New Jersey: John Wiley &amp; Sons, Inc., 2010.<br \/>[7] \u00a0CIBSE Lifts Group, \u201cTraffic Analysis &amp; Simulation Open Forum May 2007,\u201d 2007. [Online]. Available:\u00a0<a href=\"http:\/\/www.cibseliftsgroup.org\/docs\/TrafficAnalysisandSimulationOpenForumReport.pdf.\">http:\/\/www.cibseliftsgroup.org\/docs\/TrafficAnalysisandSimulationOpenForumReport.pdf.<\/a>\u00a0[Accessed 16 08 2013].<br \/>[8] \u00a0Peters, \u201cPrivate Peters Research Ltd client reports,\u201d 2007-2009.<br \/>[9] \u00a0G. Barney, \u201cTowards agreed traffic design definitions,\u201d Elevator World, p. 108, February 2005.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>The Application of Simulation to Traffic Design and Dispatcher Testing Dr Richard D PetersPeters Research Ltd. This paper was presented at The 3rd Lift Symposium on Lift and Escalator Technologies 2013\u00a0in Northampton.\u00a0This web version \u00a9 Peters Research Ltd 2013. Abstract.\u00a0 Simulation is a popular traffic design tool, but there are many different ways in which [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":860,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"elementor_canvas","meta":{"footnotes":""},"class_list":["post-1087","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>The Application of Simulation to Traffic Design and Dispatcher Testing - Peters Research<\/title>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The Application of Simulation to Traffic Design and Dispatcher Testing - Peters Research\" \/>\n<meta property=\"og:description\" content=\"The Application of Simulation to Traffic Design and Dispatcher Testing Dr Richard D PetersPeters Research Ltd. This paper was presented at The 3rd Lift Symposium on Lift and Escalator Technologies 2013\u00a0in Northampton.\u00a0This web version \u00a9 Peters Research Ltd 2013. 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