Viktor Brenner, Ph.DInstitutional Research Coordinator

Waukesha County Technical College

A Quick-and-Dirty Approach to Estimating Parking Sufficiency

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Waukesha County Technical CollegeA suburban, 100% commuter “two-year”

college on the outskirts of Milwaukee, WisconsinOver 25,000 clients served in all capacities in

2007-08Almost 10,000 program studentsOver 3,400 FTE

Over 4,000 (for the first time) including Basic SkillsNo off-street or overflow parking

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Parking at WCTC• 2000 Spaces• Shared with:

• Workforce Development Center

• Harry V. Quadracci Printing Education & Technology Center

• Richard T. Anderson Education Center

• Unpredictable additional demand

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Changes affecting Fall 2007Move from 18-week to 16-week schedule

Time between classes reduced from 10 minutes to 5 minutes

Affects space turnover patternsClasses more likely to use entire period?

IGI moves into Quadracci Center ExpansionChanging student demographic

Declining enrollment but increasing FTEIncreased impact of traditional college-age

studentsDistrict demographic bubble

Different patterns of campus use

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Student Credit Load by Age

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The ProblemParking resources were strained in Fall 2007

Students “sharking” for spacesStudents parking illegally on college

thoroughfares, in loading zones, or on the grassSome administrators believed that students

were choosing to park illegally rather than in outlying lotsPhysical inspection of inventory casts doubt on

this beliefCentral question: a parking problem or a

people problem?

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Initial AssessmentDay Sections Headcou

nt

Monday   75 1315

Tuesday    86 1391

Wednesday  76 1308

Thursday  77 1360

Friday       60 1033

Sum of Enrollments from 7:30-10:30 AMDemand < 1400 carsSpaces ~ 2000No problem!

ProblemsDoes not account for

staffImplicitly assumes

students are only on campus during the hours they are in class

Not consistent with physical observations

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Wanted: A Better Way of Estimating Parking Demand

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Step 1: Extract Individual Student Schedule DetailQuery your database to

get individual student schedule detail by day of weekEarliest start timeLatest end timeSubtract to get number

of hours on campus It is helpful to round

theseStart time to the half-

hourOn-campus to the hour

Export to Excel

ID Day

Begin Time MIN

End Time MAX

Hours Here

Begin Rounded

End Rounded

10105 Mon 1130 1925 8 11.5 19.5

10845 Mon 1800 2055 3 18 21

10959 Mon 0900 1025 1 9 10.5

11915 Mon 0730 0855 1 7.5 9

11103 Mon 0930 1225 3 9.5 12.5

11581 Mon 1800 2055 3 18 21

11797 Mon 1800 2055 3 18 21

13609 Mon 0930 2155 12 9.5 22

13945 Mon 1830 2055 2 18.5 21

13946 Mon 0730 1525 8 7.5 15.5

14811 Mon 1730 2025 3 17.5 20.5

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Step 2: Create a PivotTable of Student Record Detail

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The “Trick”Create a summation series to capture who is all

likely to be on campus at a given time.Example: Who is likely to be on campus at

11AM? First class at 7:30, on campus >3½ hoursFirst class at 8:00, on campus >3 hoursFirst class at 8:30, on campus >2½ hoursFirst class at 9:00, on campus >2 hourFirst class at 9:30, on campus > 1½ hourFirst class at 10:00, on campus > 1 hourFirst class at 10:30, on campus > ½ hourFirst class at 11:00

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Step 3: Code summation series

Every half-hour you gain a row, every hour you lose a column

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Step 4: Graph Demand Curve

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Accounting for Staff470 full-time faculty and staff

MOST at Main St. campusMOST work day shift

~750 part-time facultyMOST work eveningsMANY at Main St. Campus

Because there were lots of variables involved, we estimated a general rangeAt least 300 parking spaces would be needed for staffAs many as 500 parking spaces may be needed for

staffAdded these as “danger zones” to the usage graph

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Parking Usage Estimation (Tuesday)

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It’s All About the Patterns2006-07

2007-08

2008-09

EnrollmentsTotal Undup 3,777 3,823

(+1.2%)3,695 (-3.3%)

Full-time 2,185 2,376

(+8.7%)2,536

(+6.7%)Enrollment Pattern: Time On Campus

1-2 Hours 46% 24% 20%

3-4 Hours 44% 45% 46%

5-9 Hours 9% 25% 29%

10+ Hour 1% 6% 6%

Avg Hours on Campus

2.83 4.33 4.43

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Monday

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Wednesday

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The Problem of PrognosticationParking demand projections primarily useful if

they can predict demandLate registration: students can enroll up to the 1st

day of classFall enrollment as of August 5 indicated a maximum

parking demand of around 1400 spacesIn 2006 and 2007, enrollments increased by an

additional 20% between the first week of August and the start of classes, and

Enrollment in the first week of August 2008 was running 10% higher than the first week of August 2007

Projected parking demand by applying a 20% increase over the actual enrollment on August 5

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What actually happened• Daytime course enrollments increased by ~15%

• Evening course enrollments increased by ~25%

• Late registrants may be more likely to take evening courses

• Parking didn’t become a problem

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Limitation of the ModelProjecting from partial data

Enrollment is steady enough for projections 3 weeks before term

Project a 15% increase in day enrollment, 25% in evening

Assumes students remain on-campus for the entire timeProblematic for longer

stretchesPrimarily affects the afternoon,

when enrollment is lowestOn-the-spot interviews with

students in parking lotsArrived hours before 1st class Came to campus on days where

they had no classesMay cancel out student

absences, etc.

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Benefits ObtainedWCTC was prepared for parking overflow

during the start of the Fall termStaff placed outside to direct new students to

outlying lotsSpaces designated for parking on the grass

Scheduling conflict avoidedSheriff’s driving training had been scheduled for

north lot, would have resulted in ~50 fewer spaces on the first day of class

Strategic planning affectedStrategic planning now includes parking

availability and location considerations

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