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A Quick-and-Dirty Approach to Estimating Parking Sufficiency. Viktor Brenner, Ph.D Institutional Research Coordinator Waukesha County Technical College. Waukesha County Technical College. A suburban, 100% commuter “two-year” college on the outskirts of Milwaukee, Wisconsin

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a quick and dirty approach to estimating parking sufficiency

A Quick-and-Dirty Approach to Estimating Parking Sufficiency

Viktor Brenner, Ph.D

Institutional Research Coordinator

Waukesha County Technical College

waukesha county technical college
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
parking at wctc
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

changes affecting fall 2007
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
the problem
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?
initial assessment
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
step 1 extract individual student schedule detail
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
the trick
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
step 3 code summation series
Step 3: Code summation series

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

accounting for staff
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
the problem of prognostication
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
what actually happened
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

limitation of the model
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.
benefits obtained
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