Viktor Brenner, Ph'D

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A Quick-and-Dirty Approach to Estimating Parking
Sufficiency

• Viktor Brenner, Ph.D
• Institutional Research Coordinator
• Waukesha County Technical College

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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

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(No Transcript)
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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 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

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Student Credit Load by Age
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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?

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Initial Assessment

• Sum of Enrollments from 730-1030 AM
• Demand lt 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

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Wanted A Better Way of Estimating Parking Demand
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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

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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 730, on campus gt3½ hours
• First class at 800, on campus gt3 hours
• First class at 830, on campus gt2½ hours
• First class at 900, on campus gt2 hour
• First class at 930, on campus gt 1½ hour
• First class at 1000, on campus gt 1 hour
• First class at 1030, on campus gt ½ hour
• First class at 1100

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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 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

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Parking Usage Estimation (Tuesday)
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Its All About the Patterns
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Monday
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Wednesday
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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

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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 didnt become a problem

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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.

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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
• Sheriffs 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