Katie Wagner Named NAIOP MD|DC Member of the Year
NAIOP MD|DC Member of the Year recipient Katie Wagner (center), pictured with colleagues William Zeid (l) and Erwin Andres (r) at the 22nd Annual Awards of Excellence Gala
Gorove Slade is pleased to announce that our colleague Katie Wagner, PE, PTOE, has been recognized as the 2024 Member of the Year by NAIOP DC|MD. This honor was presented during NAIOP’s 22nd Annual Awards of Excellence Gala on September 25 at the Waldorf Astoria in Washington, D.C. The Gala is a significant event in the national capital region, recognizing notable achievements in real estate.
Katie joined the NAIOP DC|MD Chapter’s Leadership Committee in 2021 and is also a member of the Prince George’s Counties Committee. Members commit to providing leadership in strengthening relationships, promoting quality economic growth, supporting businesses, implementing development, and sustaining our communities today and for the next generation. A key part of Katie’s service included serving on the Transportation Advisory Group for Montgomery County’s Growth and Infrastructure Policy 2024-2028 update. She helped organize stakeholders around unified goals and testified in front of the planning board and planning council on behalf of NAIOP. Earning this award underscores Katie’s contributions to the field and her commitment to excellence.
“I was honored to have received this award. Thanks to Gorove Slade and my colleagues for the support as we transform the transportation network that moves the built environment.”
As a principal at Gorove Slade and the leader of our Maryland office, Katie has demonstrated strong leadership skills and expertise in transportation planning and engineering. With extensive experience in transportation planning and engineering, Katie has led projects for various clients throughout the District of Columbia, Maryland, and Virginia. Her work encompasses traffic impact studies, traffic simulation, site access and circulation planning, roadway signing and striping plans, traffic signal design, functional parking lot and garage design, and Transportation Demand Management (TDM) planning and analysis. Katie is particularly interested in urban projects and multimodal transportation solutions, making her an asset to our team and the industry. In addition, Katie’s focus on People and Culture has helped create an environment emphasizing organization, efficiency, teamwork, and staff development.
In addition to her work at Gorove Slade, Katie is involved in several professional organizations. These include the Institute of Transportation Engineers (ITE), Urban Land Institute (ULI), Commercial Real Estate Women Network (CREW), and Maryland Building Industry Association (MBIA). Katie has completed ULI’s Leadership Institute and was recognized with the Champion Award by the DC chapter of CREW in 2022, demonstrating her commitment to professional development.
NAIOP, the National Association of Industrial and Office Properties, is the leading organization for commercial real estate development professionals. The NAIOP DC|MD chapter, founded in 1990, represents the region’s leading firms in all commercial real estate industry aspects.
We’re incredibly proud of Katie’s achievement and ongoing dedication to advancing transportation solutions in our region. Congratulations once again, Katie, on this well-deserved honor!
Tollbooth-style PUDO
Scramble-style PUDO
A scramble-style PUDO refers to when some (or all) students are being dropped off or picked up on the street, not an adjacent sidewalk, and walking between cars. For scrambles, some cars drive into a designated area, and then they all stop and don’t move again until all students arrive safely at the school or in their car at dismissal. Scrambles are often used during dismissal for schools with limited sidewalks since a scramble allows for more cars to load simultaneously.
Scramble-style PUDO
When helping plan a school, what does Gorove Slade recommend? In short, all of them. Our recommendation is to design a PUDO facility that can be flexible and work for several operational styles. Once up and running, the staff and teachers can try several and see what works best. The goal is to give them the tools they need to find the best solution.
An example is the new Cardinal Elementary School in Arlington, VA. We recommended a flexible system with ample sidewalks and a bypass lane, and once it was up and running, the facility operated a bit differently than planned. At dismissal, teachers split the facility in two, with two pick-up waiting spots – one for younger grades closer to the school and one for older ones further away. This allowed for quicker matching at dismissal times.
Afternoon pick-up at Cardinal Elementary School
PUDO Analysis
Gorove Slade handles the analysis of PUDO facilities in several ways. They are inherently tricky to analyze because some operational details are challenging to model, and the significant demand is very sensitive to variables leading to large ranges of results.
Here are three ways we approach analyzing PUDO:
Queuing Analysis/Equations
One method is to use classic queuing equations, which transportation engineers have used for decades for toll booths. They are based on three factors: the arrival rate of cars, the number of booths, and the processing speed of the toll. All three of these factors correspond to PUDO facilities.
Even so, queuing equations often fail to get accurate results for PUDO facilities. For example, we were working for a private school with a notorious PUDO problem at dismissal, so we went to the field and measured the arrival rate of cars, the number of vehicles that could load simultaneously, and the average time for each pick-up. We then entered that information into our queuing models, which then told us the queue should be negative, or in other words, there shouldn’t be a queue at all, as the car arrival rate was less than the overall number of cars that could be processed.
Subsequently, we returned to our observation notes and video. We realized the longest queue in the field was when dismissal began and that our model was correct in that the queue was being processed faster than additional cars arrived. Parents and guardians arrived so early that they stacked up well beyond the school property, but once dismissal started, the queue only got shorter as more cars showed up.
The lesson we learned here is that there are more factors in the queuing analysis than just the traditional three and that arrival rates are not random.
Comparable Analysis
A common transportation engineering practice is to study comparable locations, and sometimes, that works well for PUDO facilities, especially when queuing equations don’t work as described above. We’ve taken max queue data at several private and public schools. We can try to match the car length per student ratio from a site comparable to the one we’re working on, given the design and operational elements of their PUDO.
There are two issues with using comparable data. The first one is that there’s an extensive range of data, so using our observed data leads to a max queue range of 0.10 to 0.20 cars per student being picked up. The wide range is due to how well the PUDO processes traffic and the starting queue length. To use these ratios, you need to estimate how well the PUDO will operate within this range.
But more importantly, our observations found some schools with a max queue under the 0.10 cars per student range during dismissal. This wasn’t because they had fewer cars picking up students; it was because the cars weren’t all in the same place. For example, the school we observed once had around 25 to 30 cars picking up simultaneously, but only six were at the official pick-up spot at the front door. The others were in the parking lot or curbside in several locations. So, when planning PUDO facilities, the ability of parents to use informal locations near the school can be a huge factor in the max queues and overall PUDO operations.
VISSIM Modeling
When something other than engineering judgment combined with the two analyses stiles above is desired, we turn to detailed traffic models using the VISSIM software platform. VISSIM models are highly detailed and can account for things like starting queues and varying arrival rates. The main drawback is that they require more time and resources to assemble, and in the end, they still can’t arrive at a perfect representation of a PUDO since human behavior is always a factor.
Thoughtful design and operations can dramatically improve the pick-up and drop-off process. Whether planning a brand new PUDO experience or improving existing operations, the principles and methods discussed here provide a framework for tackling one of the most persistent logistical challenges for administrators and parents alike. By considering key factors like demand patterns, operational strategies, and facility types, schools can create systems that minimize queuing, reduce neighborhood impacts, and make the beginning and end of the school day better for all.
