1. Viktor Brenner, Ph.D Institutional Research Coordinator Waukesha County Technical College A Quick-and-Dirty Approach to Estimating Parking Sufficiency 1

2. Waukesha County Technical College A suburban, 100% commuter “two-year” college on the outskirts of Milwaukee, Wisconsin Over 25,000 clients served in all capacities in 2007-08 Almost 10,000 program students Over 3,400 FTE Over 4,000 (for the first time) including Basic Skills No off-street or overflow parking 2

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4. 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 4

5. Changes affecting Fall 2007 Move from 18-week to 16-week schedule Time between classes reduced from 10 minutes to 5 minutes Affects space turnover patterns Classes more likely to use entire period? IGI moves into Quadracci Center Expansion Changing student demographic Declining enrollment but increasing FTE Increased impact of traditional college-age students District demographic bubble Different patterns of campus use 5

6. Student Credit Load by Age 6

7. The Problem Parking resources were strained in Fall 2007 Students “sharking” for spaces Students parking illegally on college thoroughfares, in loading zones, or on the grass Some administrators believed that students were choosing to park illegally rather than in outlying lots Physical inspection of inventory casts doubt on this belief Central question: a parking problem or a people problem? 7

8. Initial Assessment DaySectionsHeadcount Monday 751315 Tuesday 861391 Wednesday 761308 Thursday 771360 Friday 601033 Sum of Enrollments from 7:30-10:30 AM Demand < 1400 cars Spaces ~ 2000 No problem! Problems Does not account for staff Implicitly assumes students are only on campus during the hours they are in class Not consistent with physical observations 8

9. Wanted: A Better Way of Estimating Parking Demand 9

10. Step 1: Extract Individual Student Schedule Detail Query your database to get individual student schedule detail by day of week Earliest start time Latest end time Subtract to get number of hours on campus It is helpful to round these Start time to the half-hour On-campus to the hour Export to Excel IDDay Begin Time MIN End Time MAX Hours Here Begin Rounded End Rounded 10105Mon11301925811.519.5 10845Mon1800205531821 10959Mon090010251910.5 11915Mon0730085517.59 11103Mon0930122539.512.5 11581Mon1800205531821 11797Mon1800205531821 13609Mon09302155129.522 13945Mon18302055218.521 13946Mon0730152587.515.5 14811Mon17302025317.520.5 10

11. Step 2: Create a PivotTable of Student Record Detail 11

12. 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 hours First class at 8:00, on campus >3 hours First class at 8:30, on campus >2½ hours First class at 9:00, on campus >2 hour First class at 9:30, on campus > 1½ hour First class at 10:00, on campus > 1 hour First class at 10:30, on campus > ½ hour First class at 11:00 12

13. Step 3: Code summation series Every half-hour you gain a row, every hour you lose a column 13

14. Step 4: Graph Demand Curve 14

15. Accounting for Staff 470 full-time faculty and staff MOST at Main St. campus MOST work day shift ~750 part-time faculty MOST work evenings MANY at Main St. Campus Because there were lots of variables involved, we estimated a general range At least 300 parking spaces would be needed for staff As many as 500 parking spaces may be needed for staff Added these as “danger zones” to the usage graph 15

16. Parking Usage Estimation (Tuesday) 16

17. It’s All About the Patterns 2006-072007-082008-09 Enrollments Total Undup 3,777 3,823 (+1.2%) 3,695 (-3.3%) Full-time2,185 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% Avg Hours on Campus 2.834.334.43 17

18. Monday 18

19. Wednesday 19

20. The Problem of Prognostication Parking demand projections primarily useful if they can predict demand Late registration: students can enroll up to the 1 st day of class Fall enrollment as of August 5 indicated a maximum parking demand of around 1400 spaces In 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 20

21. 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 21

22. Limitation of the Model Projecting 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 time Problematic for longer stretches Primarily affects the afternoon, when enrollment is lowest On-the-spot interviews with students in parking lots Arrived hours before 1 st class Came to campus on days where they had no classes May cancel out student absences, etc. 22

23. Benefits Obtained WCTC was prepared for parking overflow during the start of the Fall term Staff placed outside to direct new students to outlying lots Spaces designated for parking on the grass Scheduling conflict avoided Sheriff’s driving training had been scheduled for north lot, would have resulted in ~50 fewer spaces on the first day of class Strategic planning affected Strategic planning now includes parking availability and location considerations 23