  On-street parking is convenient, but has many disadvantages: on-street spots
come at the expense of other road uses such as traffic lanes, transit lanes,
bike lanes, or parklets; drivers looking for parking contribute substantially
to traffic congestion and hence to greenhouse gas emissions; safety is reduced
both due to the fact that drivers looking for spots are more distracted than
other road users and that people exiting parked cars pose a risk to cyclists.
These social costs may not be worth paying when off-street parking lots are
nearby and have surplus capacity. To see where this might be true in downtown
Vancouver, we used artificial intelligence techniques to estimate the amount of
time it would take drivers to both park on and off street for destinations
throughout the city. For on-street parking, we developed (1) a deep-learning
model of block-by-block parking availability based on data from parking meters
and audits and (2) a computational simulation of drivers searching for an
on-street spot. For off-street parking, we developed a computational simulation
of the time it would take drivers drive from their original destination to the
nearest city-owned off-street lot and then to queue for a spot based on traffic
and lot occupancy data. Finally, in both cases we also computed the time it
would take the driver to walk from their parking spot to their original
destination. We compared these time estimates for destinations in each block of
Vancouver's downtown core and each hour of the day. We found many areas where
off street would actually save drivers time over searching the streets for a
spot, and many more where the time cost for parking off street was small. The
identification of such areas provides an opportunity for the city to repurpose
valuable curbside space for community-friendly uses more in line with its
transportation goals.
