Transportation Abroad: Exploring Innovative Infrastructure in Central and Eastern Europe
Not only was it exciting to experience this new-to-me infrastructure, but what I observed can also inform my work. After all, some of these innovations may make their way stateside someday. Without further ado, here’s the list.
1. Look closely: This red traffic signal bulb in Tskaltubo, Georgia, is shaped like a heart! So whimsical and fun! All three countries I visited follow the Vienna Convention on Road Signs and Signals, an international set of standards for traffic signals. It is used in Europe, Southeast Asia, Africa, and South America. The United States is not a signatory to this convention; we have our standards through the Manual on Uniform Traffic Control Devices (MUTCD) and various state and local standards. As far as I know, the only shapes allowed to be displayed on a signal head are circular solid arrows and U-Turn arrows.
2. I noticed many narrow streets in the older quarters of cities have been converted to one-way with small bollard-like objects. Here are two examples: the first is in Sultanahmet, Istanbul, and the other is a converted roundabout at Mozartplatz in Vienna, Austria. Where I work in the Washington, DC, area, traffic calming measures such as curb extensions, narrower lane widths, flexible posts, and speed bumps are popular. However, bollards are seldom used in the roadway because they can hinder emergency vehicles.
3. We conduct many parking studies here at Gorove-Slade. Still, I don’t know if we’ve ever studied or had a provision for dog parking like the one this supermarket in Kutaisi, Georgia, provides! While an underground parking space costs, on average, between $50,000 and $75,000 here in DC, installing this hook is relatively easy and inexpensive. Unfortunately, I don’t foresee DDOT asking for dog parking studies anytime soon.
4. The metro in Tbilisi, Georgia, is similar to the one in DC, including super–deep stations. It was built back when Georgia was a part of the Soviet Union, which was known for its deep metro construction and elaborate station design. My hometown of Wheaton, MD, is home to the longest escalator in the Western Hemisphere at 230 feet in length. The stations in Tbilisi are nearly as deep, but the escalators move twice as fast! One nice feature of this metro is the clock at the edge that shows the current time when the last train left and when the next train is set to arrive. And yes, the train comes every 5 minutes!
5. This new bike boulevard in Vienna looks similar to some shared curbless streets we have designed recently in DC, but the whole street is paved in a different color. Shared streets are becoming popular in commercial districts with much foot traffic and deliveries. I asked someone about this boulevard, and they said that the signals are all set to 10 mph bike travel so you can hit all the green lights when biking. The added benefit is that cars on this street never travel too fast, so it is not a through route. Locally, DC just changed timing for the first time on I (Eye) St, SW, which should promote safety for all users along that corridor.
6. This rest stop off the main freeway in Georgia is the most extraordinary rest stop I have ever seen. They have gas pumps, a convenience store, a food court, and even some public art! Although we have really amazing art featured in the Metro system, I think our highway rest areas and welcome centers could benefit from more creativity in design.
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.
