  To provide real-time parking information, existing studies focus on
predicting parking availability, which seems an indirect approach to saving
drivers' cruising time. In this paper, we first time propose an on-street
parking recommendation (OPR) task to directly recommend a parking space for a
driver. To this end, a learn-to-rank (LTR) based OPR model called OPR-LTR is
built. Specifically, parking recommendation is closely related to the "turnover
events" (state switching between occupied and vacant) of each parking space,
and hence we design a highly efficient heterogeneous graph called ESGraph to
represent historical and real-time meters' turnover events as well as
geographical relations; afterward, a convolution-based event-then-graph network
is used to aggregate and update representations of the heterogeneous graph. A
ranking model is further utilized to learn a score function that helps
recommend a list of ranked parking spots for a specific on-street parking
query. The method is verified using the on-street parking meter data in Hong
Kong and San Francisco. By comparing with the other two types of methods:
prediction-only and prediction-then-recommendation, the proposed
direct-recommendation method achieves satisfactory performance in different
metrics. Extensive experiments also demonstrate that the proposed ESGraph and
the recommendation model are more efficient in terms of computational
efficiency as well as saving drivers' on-street parking time.
