FIVE- M IN U T E A N A LYST
Figure 2: Histogram of raw parking meter data. Note the tri-modal nature of the data. “Overtime,” i.e.,
flashing parking meters are represented by -1 in the red-shaded oval and constitute the large bar at the
origin of the graph. Known paid parking meters are at the right and have a blue oval.
from the meters, which is displayed for
anyone who wishes to see.
What we found was surprising.
We expected to see uncorrelated
parking lot data. We did not expect to
find many over-time parking spots. I
hoped that the data would be exponential – which would lead to nice, clean
analysis. What we discovered was, well,
a mess.
Of the 100 parking spots surveyed,
25 percent were “flashing” or over-time
(violation). Of the parking spots that
were not over-time, six showed times
over one hour, implying that the persons
parked there had in fact put money in the
meter. We are completely discarding the
possibility that someone would park in a
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spot that had been previously occupied
but was not vacated, i.e., showing up
with 30 minutes remaining on meter and
not pressing the button/inserting coins. I
had hoped that the sojourn times would
be exponentially distributed, but that is a
case that is pretty difficult to make with
this dataset (see Figure 2).
Now, we don’t actually know how
many patrons have paid, or how many
have simply run over. However, there
are 100 parking spots considered, and
of these, six currently have clocks over
one hour. We can (crudely) estimate [2]
the true number of paid parking spots by
realizing that we are observing the last
hour of what may be a two-hour process. Therefore, we think approximately
W W W. I N F O R M S . O R G