  Real-time parking occupancy information is critical for a parking management
system to facilitate drivers to park more efficiently. Recent advances in
connected and automated vehicle technologies enable sensor-equipped cars (probe
cars) to detect and broadcast available parking spaces when driving through
parking lots. In this paper, we evaluate the impact of market penetration of
probe cars on the system performance, and investigate different parking
guidance policies to improve the data acquisition process. We adopt a
simulation-based approach to impose four policies on an off- street parking lot
influencing the behavior of probe cars to park in assigned parking spaces. This
in turn effects the scanning route and the parking space occupancy estimations.
The last policy we propose is a near-optimal guidance strategy that maximizes
the information gain of posteriors. The results suggest that an efficient
information gathering policy can compensate for low penetration of connected
and automated vehicles. We also highlight the policy trade-off that occur while
attempting to maximize information gain through explorations and improve
assignment accuracy through exploitations. Our results can assist urban policy
makers in designing and managing smart parking systems.
