Mobility Maze: An Intentionally Playful Approach to Transportation Planning
Tysons Community Alliance (TCA) recently hosted an interactive Placemaking Fest, showcasing innovative approaches to community engagement and urban design. A standout feature of the event was the “Mobility Maze” traffic garden, a Gorove Slade-sponsored activity that playfully merged transportation planning concepts with interactive learning for the youngest of transportation enthusiasts. This miniature urban street network, complete with lanes, stop signs, sidewalks, and even a railroad crossing, offered young participants a hands-on experience navigating a scaled-down cityscape on scooters and balance bikes.
The Mobility Maze – officially known as StreetSmarts at the PARC – exemplifies a growing trend in suburban markets: integrating educational elements about transportation safety and urban navigation into community events. It’s not just about learning for industry partners, but about creating awareness and fostering safe habits among the youngest community members. Questions like “How do I use the crosswalk?” and “What is a roundabout?” were explored in a fun, engaging manner. This approach to community education aligns with our broader placemaking vision of demonstrating how thoughtful design can contribute to community safety and vibrant public spaces.
The success of the Mobility Maze extends beyond its immediate impact at the event. Fionola Quinn, a notable figure in traffic garden design across the U.S., attended and interacted with the installation. This recognition underscores the potential for such initiatives to influence broader discussions on urban planning and community engagement. Moreover, the positive reception from Fairfax County staff and Board Supervisors highlights the importance of these creative approaches in suburban development strategies. And a special shoutout to Tech Painting Company for their incredible work in co-sponsoring and bringing this shared vision to life.
As urban planners and community leaders look to the future, initiatives like the Mobility Maze traffic garden offer valuable insights into effective placemaking. They demonstrate how targeted activities can generate interest in broader urban visions while addressing practical concerns like safety and community cohesion. For the transportation planning community, this event serves as a reminder of the multifaceted nature of our work – where technical expertise meets community engagement, and where the seeds of future urban landscapes are sown in the imaginations of the youngest residents.
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.
