Proceedings of the 2020 Winter Simulation Conference K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, eds.

Yusuke Legard
Nate Ivey

297 Herndon Parkway, Suite 302
Herndon, VA 20170, USA

Antonio R. Rodriguez
Joseph Wolski

Office of Research Services
National Institutes of Health
31 Center Dr. Bethesda, MD 20892, USA


As with many organizations, the National Institutes of Health (NIH) has seen a dramatic shift to remotework due to the COVID-19 pandemic. The NIH main campus in Bethesda, Maryland operates a shuttlebus system to take employees between key buildings, along with transporting employees from off-sitelocations in Montgomery County, Maryland. NIH has utilized simulation modeling to understand theimpact of shifting bus schedules and reduced vehicle capacity under varying passenger demand. Thissimulation tool can be used to understand how bus schedules may need to be altered to accommodatestaggered work patterns and how bus frequency should increase as workers begin returning to the NIHcampus.


The NIH main campus, is located in Bethesda, MD. It is home to more than 75 buildings on over 300acres. Shuttle services are provided to assist employees, patients, contractors, and visitors in navigatingwithin the campus. The shuttle service also provides additional routes between key off-campus locations,such as airports, Metro stops and satellite facilities

As with many organizations, the NIH campus quickly shifted to a remote work model in spring of 2020when the COVID-19 pandemic started to impact the United States. This resulted in reduced demand forshuttle buses

As NIH formulates plans to bring employees back to campus in a safe way, the office of researchservices (ORS), which provides support services to enable the research mission of the NIH, realized thedemand for shuttle services may look very different in a post-COVID or transitioning to a post-COVIDenvironment. Some of the differences may include:

  • Overall decrease in ridership due to remote work or employees wanting to avoid publictransportation.
  • Changes in the pattern of demand over the course of the day, as work schedules are staggeredto reduce congestion in key areas.
  • Changes in the origin to destination patterns of riders, as some departments/buildings may bemore likely to work remotely than others.
  • A need to proactively limit the capacity of buses to ensure adequate spacing between riders.
The ORS and MOSIMTEC built a discrete event simulation model in SIMIO in order to understand theimpact of various shuttle bus strategies for a wide variety of demand patterns.


The SIMIO-based simulation model includes a 3D animation of the NIH campus, with routes and shuttlestops drawn to scale. Passenger entities arrive with a demand pattern that varies over the course of the day.Entities randomly select an origin shuttle stop and required destination. The logic is intelligent enough forpassengers to skip getting on a shuttle to their destination, in order to wait for another shuttle that will bearriving soon and be able to get to their destination faster due to traveling a different path

Shuttle entities follow a detailed schedule with start and stop times, along with detailed time checkswhere they will be departing stops. This mimics the real system, where buses may dwell at a stop until theirpublished departure time. The model is programmed to build out a full bus schedule, based on user definedinputs, such as number of buses on a route, the start and stop time of buses on a route, and stops along aroute. This allows the user to quickly test out various shuttle scheduling strategies without having toexplicitly build a full day’s shuttle schedule for each one

The model also accounts for riders utilizing wheelchairs and scooters. Each shuttle has a user-definedcapacity for each rider type, as well as, separate times for each rider type to board or disembark

All model inputs, including shuttle schedule parameters, arrival patterns, overall demand, and delaytimes, were configurable via model input parameters or Excel input tables. This was very important giventhe level of flexibility NIH needed in evaluating extreme demand patterns that could be triggered by theCOVID work patterns

The key metrics reported by the model, both as point values and as graphs over time, included:

  • Passenger wait times by bus stop and passenger type
  • Bus fill rates by route chronologically
  • Count of times passengers did not get on a bus because it was full


The NIH shuttle bus simulation model enabled NIH to test out various shuttle strategies for a variety ofdemand patterns. Given the unknown nature of COVID’s impact on work patterns, simulation modelingwas an ideal approach, as model inputs can be changed to play what-if analysis and understand performanceunder a wide range of demand scenarios. For example, with a given bus schedule, NIH can ask the question“What happens if we have significantly more people return to work faster than expected? Does the planstill accommodate that? At what ridership level does our planned shuttle bus strategy stop working?”The visual nature of the simulation model is also critical to explain the model to non-technicalresources. The 3D animation allows stakeholders to connect with and visualize the operations withouthaving to fully understand the details of routes or shuttle metrics.