By Mike Spack, PE, PTOE
In the story of Goldilocks and the Three Bears, Goldilocks ransacks the home of the bears and eventually goes to sleep in the bed that is “just right.” Like Goldilocks picking a bed, we developers, owners, and designers like our properties to be just right. That includes having a parking supply that closely matches the parking demand.
Providing too much parking is wasteful. Each surface parking stall costs a few thousand dollars to build (stalls underground or on ramps skyrocket to $20,000+) and takes up about 400 square feet of property. Plus, the extra pavement produces water runoff that has to be routed and treated.
On the other hand, not having enough parking is also a problem. Parking may spill into neighborhoods or onto other private property, or worse yet, people may avoid the property because of the perceived hassle.
The Institute of Transportation Engineers recognized the need for data on parking demand more than thirty years ago and produced a report titled Parking Generation. My mentor Shelly Johnson led that first effort!
Parking Generation is a dataset of observed parking demand for many different land use types. It has been very helpful in helping designers and city staff determine how much parking is just right for a proposed development.
The Fourth Edition of Parking Generation was published in 2010, meaning its dataset is getting stale (really stale in that some of the data is from the 1980’s). As part of our efforts at TripGeneration.org we keep up to date parking generation for certain land uses as we collect trip generation data.
Here’s a comparison of some land uses that we’ve found to have changed significantly:
Peak Parking Rates Comparison of TripGeneration.org vs. ITE’s Parking Generation, 4th Edition
|
Land Use |
Unit |
TripGeneration.org |
ITE Parking Generation, 4th |
% Change |
| Community Center | KSF | 1.49 | 3.20 | -115% |
| Senior Living Center | Dwelling Units | 0.28 | 0.59 | -112% |
| General Office | KSF | 1.42 | 2.84 | -99% |
| Warehouse | KSF | 0.26 | 0.51 | -94% |
| Medical-Dental Office | KSF | 1.71 | 3.20 | -87% |
| Tire Store | KSF | 2.70 | 5.00 | -85% |
| Single Family Homes | Dwelling Units | 1.00 | 1.83 | -83% |
| Apartments | Dwelling Units | 0.68 | 1.23 | -81% |
| Townhomes | Dwelling Units | 0.90 | 1.38 | -53% |
| Daycare Center | KSF | 2.19 | 3.16 | -45% |
| Liquor Store | KSF | 2.25 | 2.98 | -33% |
| Pharmacy | KSF | 2.11 | 2.39 | -13% |
| Restaurant | KSF | 12.17 | 13.30 | -9% |
| Supermarket | KSF | 3.62 | 3.78 | -5% |
| Fast Food | KSF | 11.41 | 9.98 | 13% |
| Bank w/Drive Thru | KSF | 5.06 | 4.00 | 21% |
| Elementary School | Students | 0.22 | 0.17 | 24% |
| Sporting Goods Superstore | KSF | 2.40 | 1.78 | 26% |
| Middle School | Students | 0.17 | 0.09 | 48% |
Notes: ITE Rates are average peak parking rates on a weekday during the non-holiday period if there are alternatives. TripGeneration.org rates are an average of the peak parking rates.
You can dig into the whole dataset by downloading our trip generation or parking generation data at TripGeneration.org.
You might also like to read about a Case Study about Right Sizing Parking or watch a Traffic Corner Tuesday webinar where we discuss our process and a real-life case study for justifying a 40% reduction in parking at a large infill project.
Interested in more interesting traffic discussions? Checkout our free monthly webinar series, Traffic Corner Tuesday.
Great article. I look at parking a lot reviewing development applications. Do you find that parking rates fluctuate with a more frequent transit service; 15-min service vs hourly service. Do you have any comparisons like this? Does the TripGeneration.org quantify location of development; whether its in a CBD or urban setting?
Thanks, Chris
Chris – We did not document nearby transit in our dataset, so I don’t have any data to answer your question related to frequency. We enter city/state in our dataset, but don’t break out area type. Almost all of the data we collect is outside of the CBD. It’s very hard/time consuming to capture trip and parking generation data within a CBD (or even a mixed use development). It’s easy to capture the data when its a discrete use with its own parking lot in the suburbs. Mike