Onno Zoeter, Chris Dance, Stéphane Clinchant, Jean-Marc Andreoli
KDD 2014, New York City, USA, August 24-27, 2014.
On-street parking, just as any publicly owned utility, is used
inefficiently if access is free or priced very far from mar-
ket rates. This paper introduces a novel demand manage-
ment solution: using data from dedicated occupancy sensors
an iteration scheme updates parking rates to better match
demand. The new rates encourage parkers to avoid peak
hours and peak locations and reduce congestion and under-
use. The solution is deliberately simple so that it is easy
to understand, easily seen to be fair and leads to parking
policies that are easy to remember and act upon. We study
the convergence properties of the iteration scheme and prove
that it converges to a reasonable distribution for a very large
class of models. The algorithm is in use to change parking
rates in over 6000 spaces in downtown Los Angeles since
June 2012 as part of the LA Express Park project. Initial
results are encouraging with a reduction of congestion and
underuse, while in more locations rates were decreased than increased.
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