Elevator traffic analysis determines how many cars a hospital needs, how large and fast they must be, and how they are grouped into banks so that patients, staff, supplies, and waste move efficiently and without dangerous or undignified intermingling. In a multi-story hospital, getting this wrong is not merely an inconvenience: it produces stretcher queues at the elevator lobby, delayed surgical turnover, contaminated-clean cross-traffic, and a building that cannot expand. This article covers the quantitative sizing methodology and the qualitative clinical-flow separation logic that together drive the vertical-transport count and configuration.

Sizing is the how many and how big question. Flow separation is the who rides with whom question. The two are inseparable in a hospital because separating flows multiplies the number of dedicated cars beyond what raw passenger volume alone would suggest.

Why hospital elevator sizing differs from commercial buildings

Standard commercial elevator traffic analysis — the kind used for office towers — optimizes for one metric: moving the maximum number of able-bodied passengers during the morning up-peak. Hospitals break almost every assumption behind that model:

• The dominant "passenger" is often a bed or stretcher, not a standing person. A single occupied ICU bed with a ventilator, IV poles, monitor, and two-to-four accompanying staff fills an entire car. Throughput measured in "persons per car" is meaningless; the controlling unit is the bed/stretcher trip.
• There is no single peak. Hospitals run 24/7 with overlapping demand profiles — shift change, surgical start times, meal/tray delivery, visiting hours, patient transport for imaging and procedures, materials and pharmacy distribution, and waste/linen collection — each peaking at different hours and on different car types.
• Dwell times are long and unpredictable. Loading and unloading a bed, holding doors for a code team, or waiting while a patient is repositioned produces door-open times many times longer than a commercial car. These long dwells dramatically reduce effective handling capacity.
• Many trips cannot be shared. An isolation patient, a deceased patient en route to the morgue, a soiled-linen cart, or a sterile case cart should not share a car with the public or with clean supplies. Each separation requirement effectively removes that trip from the shared pool.
• Reliability and resilience carry life-safety weight. A stuck car in an office building is an inconvenience; in a hospital it can strand a coding patient between floors. Redundancy is sized for availability under failure, not just average demand.

The practical consequence: hospital elevator counts are typically driven by bed/stretcher movement and service logistics, not by visitor headcount, and the building usually needs more cars, in more separate groups, than a commercial building of equal gross area.

Inputs to a hospital elevator traffic study

A credible traffic study is built from operational data, not rules of thumb alone. The core inputs include:

• Program and stacking. Floor-by-floor functional program — number and type of beds per floor, location of the OR suite, imaging, ED, central sterile (CSPD/SPD), pharmacy, lab, kitchen/nutrition, materials management, loading dock, morgue, and the relationship of inpatient floors to diagnostic/treatment (D&T) floors. Vertical adjacency (which departments are stacked over which) is the single biggest driver of trip patterns.
• Population counts. Licensed beds, projected average daily census, peak staffing per shift, expected visitors per bed, and outpatient/procedural volumes.
• Movement events by category. Estimated daily and peak-hour counts for: inpatient transports to/from procedures and imaging, OR case-cart deliveries and returns, meal-tray cart trips, pharmacy and supply distribution rounds, linen (clean and soiled), regulated medical waste and trash, specimen and blood-product runs (where not handled by tube), and decedent transport.
• Travel distance and stops. Number of floors served, floor-to-floor heights, and the express vs. local pattern (e.g., a service group that must reach the dock, kitchen, CSPD, and every inpatient floor).
• Operational policies. Whether the facility batches deliveries, uses dedicated transport teams, restricts public access to certain floors/hours, and how it intends to segregate clean and soiled traffic.

These inputs feed a demand model — usually built and run by the elevator consultant using specialized traffic-simulation software — that tests candidate configurations against time-of-day demand and reports waiting time, transit time, and handling capacity for each car group.

Sizing methodology and the metrics that govern it

Two complementary methods are used together: