{"id":901,"date":"2020-11-17T10:48:54","date_gmt":"2020-11-17T10:48:54","guid":{"rendered":"https:\/\/wordpress.peters-research.com\/?page_id=901"},"modified":"2020-11-17T10:51:01","modified_gmt":"2020-11-17T10:51:01","slug":"analysis-of-elevator-performance-and-passenger-demand-with-destination-control","status":"publish","type":"page","link":"https:\/\/wordpress.peters-research.com\/index.php\/papers\/analysis-of-elevator-performance-and-passenger-demand-with-destination-control\/","title":{"rendered":"Analysis of Elevator Performance and Passenger Demand with Destination Control"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"901\" class=\"elementor elementor-901\">\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=\"8063\" data-id=\"2bd44fca\" 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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\">Analysis of Elevator Performance and Passenger Demand with Destination Control\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 Peters, Peters Research Ltd.<br \/>Mr Rory Smith, ThyssenKrupp Elevator, Inc.<\/p><p><strong>Key Words:<\/strong>\u00a0Destination control, destination dispatch, traffic analysis, survey, simulation<\/p><p><em>This paper was presented at ELEVCON\u00a0THESSALONIKI 2008, The International Congress on Vertical Transportation Technologies\u00a0and first published in the IAEE book &#8220;Elevator Technology 17&#8221;, edited by A. Lustig.\u00a0 It is reproduced with permission from The International Assocication of Elevator Engineers.\u00a0This web version \u00a9 Peters Research Ltd 2009.<\/em><\/p><h3>Abstract<\/h3><p>With destination control, passengers are asked to enter their destinations individually on the landings.\u00a0 The system responds by telling each passenger which elevator to use.\u00a0 Logging of destination control system data allows the operation of the elevators and passengers movements be re-played and analysed as if running a simulation.\u00a0 This assists in verifying and enhancing system performance.\u00a0\u00a0 Calls logged by destination control correlate better with actual passenger demand than calls logged by conventional control.\u00a0 Data from a modernised elevator installation is used to assess elevator performance before and after a modernisation.<\/p><h3>1.\u00a0\u00a0\u00a0\u00a0 Introduction<\/h3><p>Most conventional elevator systems have up and down hall call buttons at each floor, except at the top and bottom floors where only one button is required.\u00a0 With destination control, each passenger is invited to enter their destination floor at the landing and the system responds by indicating which elevator to use.\u00a0 ThyssenKrupp TAC50 Destination DispatchTM [1][2] can operate with destination input, conventional up and down hall call buttons, or a combination of all of these.<\/p><p>In this paper we review the logging of elevator performance and passenger demand in a high rise building in North America operating TAC50 Destination Dispatch.\u00a0 In this installation, the elevators are operating as a full destination control system.\u00a0 Destination input is provided on every floor with touch screens.<\/p><p>Logging destination control operation, it is easier to monitor passenger demand than with conventional traffic analysers, as every passenger is asked to register a call [3].\u00a0 Destination control also provides an opportunity to measure the quality of service from the passengers\u2019 prospective, as it is known when every call is registered, answered, and completed.<\/p><h3>2.\u00a0\u00a0\u00a0\u00a0 Logging<\/h3><h4>2.1\u00a0\u00a0\u00a0\u00a0 Data collection and playback<\/h4><p>ThyssenKrupp TAC50 Destination Dispatch elevators include logging software that can record every movement of the elevators and every passenger destination call.\u00a0 This is similar to the approach presented by Beebe [4], but a proprietary ThyssenKrupp data exchange format has been used to reduce file size.\u00a0 The logging data can be replayed in Elevate[TM] as if the user is running a simulation as shown in Figure 1.\u00a0 The playback can be paused at any time to examine in detail current elevators status and calls.<\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/Analysisofelevatorperformance\/image002.jpg\" alt=\"\" border=\"0\" \/><\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/Analysisofelevatorperformance\/image004.jpg\" alt=\"\" border=\"0\" \/>\u00a0<img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/Analysisofelevatorperformance\/image006.jpg\" alt=\"\" border=\"0\" \/><\/p><p><strong><em>Figure 1\u00a0\u00a0 Elevate replaying logging data<\/em><\/strong><\/p><p>At the same time as the logging data is being replayed, an analysis of passenger demand and elevator performance is completed. The calculated passenger traffic demand is based on the presumption that one destination call corresponds to one person.\u00a0 In future versions logged load weighing data may be used to improve on this assumption.<\/p><p>Once playback is completed, the calculated passenger demand can be used to run a simulation.\u00a0 This tests how the system should be performing based on current demand.\u00a0 In the examples following in Section 3, simulation has also been used to model operation of the old system prior to modernisation.<\/p><h4>2.2\u00a0\u00a0\u00a0\u00a0 Measures of performance<\/h4><p>Traditional traffic analysers measure hall call response time.\u00a0 This is all they can measure, as they are not aware of passengers.\u00a0 The system only \u201csees\u201d hall calls (and car calls).\u00a0 With destination control each passenger is invited to register his or her own call, so individual passenger waiting times can be measured.\u00a0 The waiting time is the time from when the call is registered until the doors, on the elevator the passenger is assigned to, begin to open.\u00a0 Time to destination (waiting time plus the transit time) can also be measured.\u00a0 Waiting time and time to destination are better measures of the quality of service a passenger receives than hall call response time as they truly reflect a passenger\u2019s \u201cexperience\u201d.\u00a0 These parameters are represented diagrammatically in Figure 2, where two passengers (P1 and P2) traveling between the same floors arrive at different times, but use the same elevator.<\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/Analysisofelevatorperformance\/image008.gif\" alt=\"\" border=\"0\" \/><\/p><p><strong><em>Figure 2\u00a0\u00a0 Waiting time and time to destination<\/em><\/strong><\/p><p>\u00a0<\/p><h3>3.\u00a0\u00a0\u00a0\u00a0 Results based on simulation and logging<\/h3><h4>3.1\u00a0\u00a0\u00a0\u00a0 All day results<\/h4><p>Figures 3, 4 and 5 show average waiting time and time to destination for a typical day in the example building.\u00a0 The results show a high level of consistency between the logged and simulated results for the new installed system.\u00a0 There is a significant improvement relative to the old system.<\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/Analysisofelevatorperformance\/image010.gif\" alt=\"\" border=\"0\" \/><br \/><strong><em>Figure 3\u00a0\u00a0 All day results for low rise elevators<br \/><\/em><\/strong><\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/Analysisofelevatorperformance\/image012.gif\" alt=\"\" border=\"0\" \/><br \/><strong><em>Figure 4\u00a0 All day results for mid rise elevators<\/em><\/strong><\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/Analysisofelevatorperformance\/image014.gif\" alt=\"\" border=\"0\" \/><br \/><strong><em>Figure 5\u00a0\u00a0 All day results for high rise elevators<\/em><\/strong><\/p><h4>3.2\u00a0\u00a0\u00a0\u00a0 Results by time of day<\/h4><p>As opposed to whole day averages, Figures 6 to 11 show the passenger demand measured by the logging on a continuous basis throughout a typical day.\u00a0 Each demand curve is followed by a plot of the measured time to destination (as logged by the system) and results for the simulation of the installed system, and the old system.\u00a0 Again, there is good consistency between the logged and simulated results for the new installed system and a significant improvement relative to the old system.<\/p><p>For passenger demand plots, traffic travelling up the building is show in the top portion of the graphs, and traffic travelling down the building is show in the bottom portion.\u00a0 There is a noticeable peak in the morning around 9 am. People travel down to take lunch out of the building with a peak around 12 noon.\u00a0 The return from lunch peaks at around 1 pm.\u00a0 There is also a noticeable down peak as people travel home at the end of the day.<\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/Analysisofelevatorperformance\/image016.gif\" alt=\"\" border=\"0\" \/><br \/><strong><em>Figure 6\u00a0\u00a0 Low rise demand<\/em><\/strong><\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/Analysisofelevatorperformance\/image018.gif\" alt=\"\" border=\"0\" \/><br \/><strong><em>Figure 7\u00a0\u00a0 Low rise time to destination<\/em><\/strong><\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/Analysisofelevatorperformance\/image020.gif\" alt=\"\" border=\"0\" \/><br \/><strong><em>Figure 8\u00a0\u00a0 Mid rise demand<\/em><\/strong><\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/Analysisofelevatorperformance\/image022.gif\" alt=\"\" border=\"0\" \/><br \/><strong><em>Figure 9\u00a0\u00a0 Mid rise time to destination<\/em><\/strong><\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/Analysisofelevatorperformance\/image024.gif\" alt=\"\" border=\"0\" \/><br \/><strong><em>Figure 10\u00a0\u00a0 High rise demand<\/em><\/strong><\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/Analysisofelevatorperformance\/image026.gif\" alt=\"\" border=\"0\" \/><br \/><strong><em>Figure 11\u00a0\u00a0 High rise time to destination<\/em><\/strong><\/p><p>\u00a0<\/p><h4>3.3\u00a0\u00a0\u00a0\u00a0 Day to day variations in demand<\/h4><p>Plotting data for a whole week demonstrates a high level of consistency in passenger demand.\u00a0 Each rise has a recognisable passenger demand pattern or \u201csignature\u201d.<\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/Analysisofelevatorperformance\/image028.gif\" alt=\"\" border=\"0\" \/><br \/><strong><em>Figure 12\u00a0 Low rise passenger demand for one week<\/em><\/strong><\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/Analysisofelevatorperformance\/image030.gif\" alt=\"\" border=\"0\" \/><br \/><strong><em>Figure 13\u00a0 Mid rise passenger demand for one week<\/em><\/strong><\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/Analysisofelevatorperformance\/image032.gif\" alt=\"\" border=\"0\" \/><br \/><strong><em>Figure 14\u00a0 High rise passenger demand for one week<br \/><\/em><\/strong><\/p><h3>4.\u00a0\u00a0\u00a0\u00a0 Further benefits of the upgraded system<\/h3><p>The new installation can move more people more quickly, partly because of faster equipment, partly because of more intelligent control.\u00a0 This makes it less likely to overload or \u201csaturate\u201d during busy periods.<\/p><p>To demonstrate this, we have used the simulation to test how the old and new systems perform when presented with a constantly increasing stream of traffic.\u00a0 This is a recognized way to test the performance of elevator systems to their limits.\u00a0 The example in Figures 15 and 16 is based on the low rise up peak.\u00a0 During this time, 63% of the traffic was incoming, 22% outgoing, and 15% interfloor.<\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/Analysisofelevatorperformance\/image034.gif\" alt=\"\" border=\"0\" \/><br \/><strong><em>Figure 15\u00a0 Waiting time for increasing traffic demand<\/em><\/strong><\/p><p><img decoding=\"async\" src=\"https:\/\/wordpress.peters-research.com\/images\/stories\/papers\/Analysisofelevatorperformance\/image036.gif\" alt=\"\" border=\"0\" \/><br \/><strong><em>Figure 16\u00a0 Time to destination for increasing traffic demand<\/em><\/strong><\/p><p>The old system begins to overload at around 100 persons per five minutes, while the new system continues to work well.\u00a0 Waiting time and time to destination increase, but the system does not \u201csaturate\u201d.\u00a0 Destination control elevators are less likely to saturate as a result of up peak traffic [5].\u00a0 The ability to cope with greater passenger demand means that the installation can manage better if there is ever a car out of service.<\/p><h3>5.\u00a0\u00a0\u00a0\u00a0 Discussion<\/h3><p>Logging to the level of detail presented in this paper provides an enormous amount of data.\u00a0 Most of the graphs used in simulation design (passenger demand, passenger activity, queue lengths, distribution of waiting times, etc.) can be generated daily.\u00a0 This provides the basis for validating the original design, the current performance of the elevators, and even specific design features.<\/p><p>It has been found that an unusually long passenger waiting time almost always indicates that something is amiss.\u00a0 Replaying the operation of the elevators during the long wait incident assists in diagnosing the problem.\u00a0 Applying this approach, hardware, software and general maintenance issues can be identified and addressed.\u00a0 Without logging, these issues could go unnoticed.<\/p><p>Operation of specific features can also be observed.\u00a0 For example, occasional passenger misuse of the elevators is observed in logging.\u00a0 In one instance 29 calls were registered at a landing to the same destination, but the load weighing recorded that only one person loaded the car.\u00a0 The logging confirmed that the dispatcher successfully marked the majority of these calls as false, revising its dispatching optimisation process accordingly.<\/p><p>Logging also increases our understanding of passenger demand, which assists both in the elevator selection process and in the design of elevator dispatchers.<\/p><h3>6.\u00a0\u00a0\u00a0\u00a0 Conclusions<\/h3><p>For the example building considered in this paper, there is a high degree of consistency between the performance measured by logging and the simulation of the installed system.\u00a0 Simulation of the old system based on existing passenger demand has also quantified the improvements achieved through modernisation.\u00a0 The improvement have been confirmed by the client, and further backed up by anecdotal evidence from people working in the building.<\/p><p>Daily logging reports delivered automatically by email provide a detailed analysis of current performance and highlight any problems to building management immediately.\u00a0 Problems can be investigate in detail by replaying the scenario leading up to incident, for example an unusually long passenger waiting time.\u00a0 This approach has highlighted software, hardware and maintenance issues, which otherwise may not have been identified.\u00a0 The detail in which the system performance can now be examined is unprecedented.<\/p><p>The full logging reports also detail the distribution of waiting times and queue lengths, in the same format as presented in an Elevate simulation.\u00a0 The consistency of presentation between simulation and logging makes it easier for those familiar with simulation to interpret results.<\/p><p><strong><br \/>REFERENCES<\/strong><\/p><ol><li>Smith R, Peters R\u00a0 ETD Algorithm with Destination Dispatch and Booster Options\u00a0\u00a0\u00a0 Elevator Technology 12, Proceedings of ELEVCON 2002 (The International Association of Elevator Engineers) (2002)<\/li><li>Smith R, Peters R Enhancements to the ETD dispatching\u00a0 Elevator Technology 14, Proceedings of ELEVCON 2004 (The International Association of Elevator Engineers) (2004)<\/li><li>Peters R, Evans E Measuring and Simulating Elevator Passengers in Existing Buildings\u00a0 Elevator Technology 17, Proceedings of ELEVCON 2008 (The International Association of Elevator Engineers) (2008)<\/li><li>Beebe J, An XML-based standard for communicating elevator information\u00a0 Elevator Technology 15, Proceedings of ELEVCON 2005 (The International Association of Elevator Engineers) (2005)<\/li><li>Peters R, Understanding the benefits and limitation of destination dispatch\u00a0 Elevator Technology 16, Proceedings of ELEVCON 2006 (The International Association of Elevator Engineers) (2006)<\/li><\/ol>\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>Analysis of Elevator Performance and Passenger Demand with Destination Control Dr Richard Peters, Peters Research Ltd.Mr Rory Smith, ThyssenKrupp Elevator, Inc. Key Words:\u00a0Destination control, destination dispatch, traffic analysis, survey, simulation This paper was presented at ELEVCON\u00a0THESSALONIKI 2008, The International Congress on Vertical Transportation Technologies\u00a0and first published in the IAEE book &#8220;Elevator Technology 17&#8221;, edited by [&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-901","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>Analysis of Elevator Performance and Passenger Demand with Destination Control - 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=\"Analysis of Elevator Performance and Passenger Demand with Destination Control - Peters Research\" \/>\n<meta property=\"og:description\" content=\"Analysis of Elevator Performance and Passenger Demand with Destination Control Dr Richard Peters, Peters Research Ltd.Mr Rory Smith, ThyssenKrupp Elevator, Inc. 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