A Quick-and-Dirty Approach to Estimating Parking Sufficiency

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

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

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

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

6. Student Credit Load by Age

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?

8. Initial Assessment • 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

9. Wanted: A Better Way of Estimating Parking Demand

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

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

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

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

14. Step 4: Graph Demand Curve

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

16. Parking Usage Estimation (Tuesday)

17. It’s All About the Patterns

18. Monday

19. Wednesday

20. The Problem of Prognostication • Parking demand projections primarily useful if they can predict demand • Late registration: students can enroll up to the 1st 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

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

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 1st class • Came to campus on days where they had no classes • May cancel out student absences, etc.

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