Urban spatio-temporal prediction is crucial for informed decision-making, such as traffic management, resource optimization, and emergence response. Despite remarkable breakthroughs in pretrained natural language models that enable one model to handle diverse tasks, a universal solution for spatio-temporal prediction remains challenging Existing prediction approaches are typically tailored for specific spatio-temporal scenarios, requiring task-specific model designs and extensive domain-specific training data. In this study, we introduce UniST, a universal model designed for general urban spatio-temporal prediction across a wide range of scenarios. Inspired by large language models, UniST achieves success through: (i) utilizing diverse spatio-temporal data from different scenarios, (ii) effective pre-training to capture complex spatio-temporal dynamics, (iii) knowledge-guided prompts to enhance generalization capabilities. These designs together unlock the potential of building a universal model for various scenarios Extensive experiments on more than 20 spatio-temporal scenarios demonstrate UniST's efficacy in advancing state-of-the-art performance, especially in few-shot and zero-shot prediction. The datasets and code implementation are released on https://github.com/tsinghua-fib-lab/UniST.

UNIST scientists have recently developed an interactive visual analytics system that enables traffic congestion exploration, surveillance, and forecasting based on vehicle detector data. Their system can predict traffic conditions for the next 5 to 15 minutes at an error rate of fewer than four kilometers an hour. This intelligent visual analytics system empowers traffic congestion exploration, observation, and determining dependent on vehicle detector information. Through domain expert collaboration, we have extricated task requirements, consolidated the Long-Short Term Memory (LSTM) model for congestion forecasting, and designed a weighting technique for distinguishing the reasons for congestion and congestion propagation directions. The system then visualized the traffic situation for easier comprehension: Congestion levels and average driving speed, for instance, are described using colors and shapes.

The Ministry of Science, ICT & Future Planning announced on June 19 that Ulsan National Institute of Science & Technology professor Choi Jae-shik recently developed an artificial intelligence system and is going to unveil it at an academic seminar this month. According to the professor, the system is capable of predicting the future prices of houses, future stock prices, foreign exchange rate movements and the like after reading newspaper articles, business reports and so on and then automatically drawing up reports in English. "The system will become capable of drawing up the same reports in Korean at some point in time next year and writing news articles in the near future," the professor remarked. Earlier, an AI system capable of stock price prediction has been developed by the MIT and the University of Cambridge. This system, however, is limited in accuracy because it predicts future prices by analyzing correlations between the prices of stocks owned by someone and the others based on numerical data such as past prices.