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Many U.S. metropolitan cities are notorious for their severe shortage ofparking spots. To this end, we present a proactive prediction-drivenoptimization framework to dynamically adjust parking prices. We usestate-of-the-art deep learning technologies such as neural ordinarydifferential equations (NODEs) to design our future parking occupancy rateprediction model given historical occupancy rates and price information. Owingto the continuous and bijective characteristics of NODEs, in addition, wedesign a one-shot price optimization method given a pre-trained predictionmodel, which requires only one iteration to find the optimal solution. In otherwords, we optimize the price input to the pre-trained prediction model toachieve targeted occupancy rates in the parking blocks. We conduct experimentswith the data collected in San Francisco and Seattle for years. Our predictionmodel shows the best accuracy in comparison with various temporal orspatio-temporal forecasting models. Our one-shot optimization method greatlyoutperforms other black-box and white-box search methods in terms of the searchtime and always returns the optimal price solution.

Prediction-based One-shot Dynamic Parking Pricing