By Mike Spack, PE, PTOE
There’s been a housing boom around the University of Minnesota for about three years. Â Very nice, new apartment buildings are being built with underground parking garages. Â The new trend seems to be that each student wants their own bedroom and most of these buildings are providing a range of unit sizes – a mix of studio to 4 bedrooms.
We’ve provided traffic studies/travel demand management plans on several of these proposed buildings.  One thing we wrestle with is what trip generation should we use?  We’ve used trip generation numbers from ITE’s Trip Generation for the generic apartment land use, but that seems high.  Enough of these buildings are finally operating now that we decided to do a little trip generation study.
We were able to track down six buildings where we could easily video their parking lot driveways AND get building statistics.  We’ve determined a.m. peak hour, p.m. peak hour, and daily trip generation rates Vs. number of dwelling units, number of parking stalls provided, and number of bedrooms provided.  Read our full study here Download Student Apartment Trip Generation Study.
Below are the quick results compared to the average rates of a generic apartment building in ITE’s Trip Generation, 8th Edition. Â It’s clear student housing generates low levels of traffic and a full traffic impact study will only be justified for very large complexes (roughly more than 400 dwelling units). Â We were not able to capture bicycle data, but in our interviews of property managers it seems bicycle parking is a much bigger transportation issue than vehicle parking/traffic impacts. Â Something designers should keep in mind.
Average Trip Generation Rates for Student Housing and Apartment per Number of Dwelling Units
|
Student Housing Apartments |
Apartment from Trip Generation, 8th Edition |
|
| Weekday |
2.82 |
6.65 |
| Weekday A.M. Peak Hour(between 7-9 a.m.) |
0.13 |
0.51 |
| Weekday P.M. Peak Hour (between 4-6 p.m.) |
0.24 |
0.62 |
Want more Trip Generation data? Check out our free, professionally collected trip generation and parking generation data at TripGeneration.org.
Cool Data, Mike. This info will be very useful for a lot of folks, I’m sure.
I wonder how applicable this is for all different types of student housing near the U. Are the students living in the newest buildings with underground parking likely to be more affluent than other students? More likely to be car owners?
Hey Reuben – Glad you like it. These are definitely units with more affluent students who are more likely to own a car. To me, the big take away is that no student housing project will really need a full traffic impact study. The design of driveways should be reviewed for sight distance and someone should look over the bike parking situation. But it would take a massive housing project to trigger the need for a turn lane or a traffic signal at an intersection near that proposed student housing.
What a timely study for me to read!. I’ll be meeting with another traffic engineer for a proposed student housing project across the street from a large university where I am the traffic engineer for the municipality. I’ll likely share your information with him as this was to be our discussion…what needs to be studied?
I’m curious what effect distance from campus would have on the trip rates. I would predict that the further an apartment complex is from campus that more trips would be generated. Thanks so much for sharing all the information!!
We in Florida DOT are in the midst of studying student apartments where there are three or four independent bedroom/bathroom suites, all joining a common area with living and kitchen space. We will be using the individual bedroom/bathroom as the independent variable.
We should have some published data in July.
One problem on your tables, shouldn’t the average rates be somewhere within the range of rates? Average is also misspelled.
I’d also like to see how these sites were for distance to campus, and transit routes. I have done TIAs for large student housing developments approx 1 mile from campus (Texas A&M) where we used apartment data per person. That worked out fairly well when actual trip gen was checked after occupancy.
We’ve updated the report in the post to put in the correct range of data on each sheet (and fix the typo).
These sites were all within a mile of campus (although campus is quite sprawling). If it’s reasonable for students to bike to campus, I’d use our new dataset. If it’s not reasonable for them to bike to campus, I’d just use normal apartment rates. Some judgment is involved.
The trend we have seen in Florida is for three and four bedroom student housing. I’d like to see the rate also per student. We recently relied upon a trip generation conducted around the Auburn University campus that was based on the number of students and also with and without transit available.
The full study has rates per bedroom, which is a reasonable substitution for students.
Mike