  Many U.S. metropolitan cities are notorious for their severe shortage of
parking spots. To this end, we present a proactive prediction-driven
optimization framework to dynamically adjust parking prices. We use
state-of-the-art deep learning technologies such as neural ordinary
differential equations (NODEs) to design our future parking occupancy rate
prediction model given historical occupancy rates and price information. Owing
to the continuous and bijective characteristics of NODEs, in addition, we
design a one-shot price optimization method given a pre-trained prediction
model, which requires only one iteration to find the optimal solution. In other
words, we optimize the price input to the pre-trained prediction model to
achieve targeted occupancy rates in the parking blocks. We conduct experiments
with the data collected in San Francisco and Seattle for years. Our prediction
model shows the best accuracy in comparison with various temporal or
spatio-temporal forecasting models. Our one-shot optimization method greatly
outperforms other black-box and white-box search methods in terms of the search
time and always returns the optimal price solution.
