Optimal “Elevatoring” for London’s Tallest Buildings
May 21, 2016
Elevator traffic simulation has become one of the most important tools in the design and optimisation of vertical transportation systems. In tall buildings, the consequences of poor lift planning are felt by occupants every day: long queues at lobby level, overcrowded cars during peak periods, and the kind of frustration that gradually erodes satisfaction with a building. The good news is that these problems are largely preventable, provided the right analysis is done at the right stage. This is where elevator traffic simulation makes the difference.
Designing an elevator system for a high-rise building is not simply a matter of installing enough cars to serve the number of floors. The challenge lies in how people move through a building, and that movement is far more complex than it might appear. Occupants do not arrive and depart in a steady, predictable flow. They cluster at peak periods, concentrate on certain floors, and behave differently depending on the time of day and the purpose of their journey.
A lift system that performs adequately under average conditions may fail entirely during morning up-peak, end-of-day down-peak, or lunchtime inter-floor traffic. Without modelling these scenarios in advance, designers are essentially guessing at a configuration that will either over-provision at significant cost or under-deliver at significant inconvenience.
Elevator traffic simulation uses computational modelling to replicate the movement of passengers through a building’s lift system across a range of real-world conditions. Rather than relying on simplified calculations based on average assumptions, simulation tools like AdSimulo model individual passenger arrivals, waiting behaviours, car dispatch logic, and system responses dynamically.
This allows designers and engineers to test different configurations before any physical commitment is made. How many lifts are needed? What should their capacity and speed be? How should they be grouped and dispatched? What happens to wait times if occupancy increases by 20 percent? Elevator traffic simulation answers these questions with data rather than approximation.
As noted in Elevator World’s overview of traffic analysis fundamentals, the core aim of elevator traffic engineering is to move a specific number of passengers from origin to destination floors with the minimum wait and travel time, using the minimum number of lifts and core space. Simulation is the most reliable method of achieving that balance in complex, multi-use buildings.
Peak traffic periods represent the most demanding test of any lift system. In a commercial office building, morning up-peak can see a significant proportion of total daily occupants arriving within a 20 to 30-minute window. In residential towers, evening down-peak presents a similar concentration. Mixed-use buildings, combining residential, office, and retail functions, create multiple overlapping peak scenarios that simple calculation methods struggle to model accurately.
Elevator traffic simulation handles these scenarios by modelling traffic conditions independently and in combination. The result is a system specification that is genuinely sized for the building’s actual demand profile, not an average that works reasonably well most of the time and poorly when it matters most.
Elevator traffic simulation is most valuable when it is integrated early into the design process, at the stage when fundamental decisions about core sizing, lift quantity, and system configuration are still open. Changes at this point cost nothing. Changes after installation are expensive, disruptive, and in many cases simply not feasible.
That said, simulation also has an important role in existing buildings. If occupancy has changed, a building has been repurposed, or an existing lift system is underperforming, simulation can identify the root cause and model the effect of potential interventions without a costly trial-and-error approach.
When running elevator traffic simulation, the metrics that matter most include:
Optimising these metrics simultaneously, rather than in isolation, is precisely what elevator traffic simulation enables. Improving one metric at the expense of another is a common pitfall in manually calculated designs.
AdSimulo provides professional elevator traffic simulation software built for engineers, architects, and vertical transportation consultants working on projects of all scales. If you are involved in the design, specification, or optimisation of a lift system and want to see what simulation-based analysis can deliver, we would be happy to walk you through the platform.
Request a demo of AdSimulo today and see elevator traffic simulation in action.
Ideally, elevator traffic simulation should be conducted during the early design phase, before core dimensions and lift quantities are fixed. This is when the findings have the greatest influence on outcomes. However, simulation is also valuable for existing buildings where performance is being reviewed or occupancy is changing.
Yes. While the traffic patterns in residential buildings differ from those in commercial office environments, the same simulation principles apply. AdSimulo models residential traffic conditions including evening down-peak and weekend patterns, providing an accurate basis for system specification.
Modern elevator traffic simulation tools, when properly configured with accurate building data and occupancy assumptions, produce results that closely reflect real-world performance. The key is using a platform that models individual passenger behaviour dynamically rather than applying static averages.
A simulation requires information about the building’s floor count and usage, the number of lifts and their technical specifications, expected occupancy levels, and the traffic patterns relevant to the building type. AdSimulo is designed to work efficiently with the data typically available at the design stage.
Yes. AdSimulo models destination dispatch control alongside conventional group control systems, allowing designers to compare the two approaches and quantify the performance benefits of destination dispatch for a specific building and traffic profile.
