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.

Token reward airdrops hope to "nudge" car users into more sustainable behaviors. Artificial intelligence specialists Fetch.ai, and blockchain solutions provider Datarella have announced the launch of a "Smart City" infrastructure trial in Munich, Germany, on Nov. 12. The trial will be centered around the Connex Buildings business center in the city and will use a multi-agent blockchain-based AI platform to optimize parking space management at the building. This is designed to encourage reduced car use, and hence CO2 emissions. Autonomous economic agents will negotiate the "price" of parking spaces between the operators and users.