Accuracy and timeliness are indeed often conflicting goals in prediction tasks.Premature predictions may yield a higher rate of false alarms, whereas delaying predictions to gather more information can render them too late to be useful.In applications such as wildfires, crimes, and traffic jams, timely forecasting are vital for safeguarding human life and property.Consequently, finding a balance between accuracy and timeliness is crucial.In this paper, we propose an early spatio-temporal forecasting model based on Multi-Objective reinforcement learning that can either implement an optimal policy given a preference or infer the preference based on a small number of samples.The model addresses two primary challenges: 1) enhancing the accuracy of early forecasting and 2) providing the optimal policy for determining the most suitable prediction time for each area.Our method demonstrates superior performance on three large-scale real-world datasets, surpassing existing methods in early spatio-temporal forecasting tasks.

Given the increasing importance of predicting spatiotemporal events such as wildfire, crime, and traffic congestion, existing methods are faced with the challenge of balancing timeliness and accuracy. Late predictions may result in tremendous economic costs and human life loss, while inaccurate predictions are likely to cause unnecessary public resources and social anxiety. Therefore, balancing accuracy and timeliness is essential in general spatiotemporal event prediction problems. In this paper, we propose an Early Spatiotemporal Graph Convolutional Network (ESTGCN)1to adaptively determine the optimal prediction time, which makes a tradeoff between prediction accuracy and timeliness and addresses two major questions: 1) How can we determine optimal prediction time points for different areas, taking into account their unique characteristics and conditions? 2) How can we minimize the propagation of prediction errors throughout the forecast timeline? Extensive experiments on two large-scale real-world datasets demonstrate that our proposed approaches can give an optimal prediction time in advance for each area and outperform all baselines in early spatiotemporal prediction tasks.

With the growing number of cars in cities, smart parking is gradually becoming a strategic issue in building a smart city. As the precondition in smart parking, accurate parking prediction can reduce the time drivers spend searching for parking spaces and relieve traffic congestion. Meanwhile, VANET and the Internet-of-things (IoT) are the key elements of the current intelligent transportation system. With the IoT devices based on VANET becoming more extensively employed, a large amount of parking data is generated every day, and various methods are proposed for parking prediction, therefore, it is time to systematically summarize the parking prediction issues and the state-of-the-art prediction methods. In this survey, we first provide a comprehensive review of the existing methods used for parking prediction ranging from conventional statistical methods to the latest graph neural network methods. Then, we classify a variety of parking problems such as parking availability prediction, parking behavior prediction, and parking demand prediction. We also compile all the evaluation metrics, open data, and open-source code of the surveyed literature. Finally, we present the challenges and future directions of the parking prediction technique. As far as we know, this is the first survey exploring parking prediction methods, which will be of interest to both researchers and practitioners engaging in intelligent transportation systems (ITS) and smart cities.

Named Entity Recognition (NER) aims to identify the pre-defined entities from the unstructured text. Compared with English NER, Chinese NER faces more challenges: the ambiguity problem in entity boundary recognition due to unavailable explicit delimiters between Chinese characters, and the out-of-vocabulary (OOV) problem caused by rare Chinese characters. However, two important features specific to the Chinese language are ignored by previous studies: glyphs and phonetics, which contain rich semantic information of Chinese. To overcome these issues by exploiting the linguistic potential of Chinese as a logographic language, we present MPM-CNER (short for Multi-modal Pretraining Model for Chinese NER), a model for learning multi-modal representations of Chinese semantics, glyphs, and phonetics, via four pretraining tasks: Radical Consistency Identification (RCI), Glyph Image Classification (GIC), Phonetic Consistency Identification (PCI), and Phonetic Classification Modeling (PCM). Meanwhile, a novel cross-modality attention mechanism is proposed to fuse these multimodal features for further improvement. The experimental results show that our method outperforms the state-of-the-art baseline methods on four benchmark datasets, and the ablation study also verifies the effectiveness of the pre-trained multi-modal representations.