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.

Building extraction is a popular topic in remote sensing image processing. Efficient building extraction algorithms can identify and segment building areas to provide informative data for downstream tasks. Currently, building extraction is mainly achieved by deep convolutional neural networks (CNNs) based on the U-shaped encoder–decoder architecture. However, the local perceptive field of the convolutional operation poses a challenge for CNNs to fully capture the semantic information of large buildings, especially in high-resolution remote sensing images. Considering the recent success of the Transformer in computer vision tasks, in this paper, first we propose a shifted-window (swin) Transformer-based encoding booster. The proposed encoding booster includes a swin Transformer pyramid containing patch merging layers for down-sampling, which enables our encoding booster to extract semantics from multi-level features at different scales. Most importantly, the receptive field is significantly expanded by the global self-attention mechanism of the swin Transformer, allowing the encoding booster to capture the large-scale semantic information effectively and transcend the limitations of CNNs. Furthermore, we integrate the encoding booster in a specially designed U-shaped network through a novel manner, named the Swin Transformer-based Encoding Booster- U-shaped Network (STEB-UNet), to achieve the feature-level fusion of local and large-scale semantics. Remarkably, compared with other Transformer-included networks, the computational complexity and memory requirement of the STEB-UNet are significantly reduced due to the swin design, making the network training much easier. Experimental results show that the STEB-UNet can effectively discriminate and extract buildings of different scales and demonstrate higher accuracy than the state-of-the-art networks on public datasets.

With the development of sensors and of the Internet of Things (IoT), smart cities can provide people with a variety of information for a more convenient life. Effective on-street parking availability prediction can improve parking efficiency and, at times, alleviate city congestion. Conventional methods of parking availability prediction often do not consider the spatial–temporal features of parking duration distributions. To this end, we propose a parking space prediction scheme called the hybrid spatial–temporal graph convolution networks (HST-GCNs). We use graph convolutional networks and gated linear units (GLUs) with a 1D convolutional neural network to obtain the spatial features and the temporal features, respectively. Then, we construct a spatial–temporal convolutional block to obtain the instantaneous spatial–temporal correlations. Based on the similarity of the parking duration distributions, we propose an attention mechanism called $distAtt$ to measure the similarity of parking duration distributions. Through the $distAtt$ mechanism, we add the long-term spatial–temporal correlations to our spatial–temporal convolutional block, and thus, we can capture complex hybrid spatial–temporal correlations to achieve a higher accuracy of parking availability prediction. Based on real-world datasets, we compare the proposed scheme with the benchmark models. The experimental results show that the proposed scheme has the best performance in predicting the parking occupancy rate.

Nowadays, people are accustomed to posting images and associated text for expressing their emotions on social networks. Accordingly, multimodal sentiment analysis has drawn increasingly more attention. Most of the existing image-text multimodal sentiment analysis methods simply predict the sentiment polarity. However, the same sentiment polarity may correspond to quite different emotions, such as happiness vs. excitement and disgust vs. sadness. Therefore, sentiment polarity is ambiguous and may not convey the accurate emotions that people want to express. Psychological research has shown that objects and words are emotional stimuli and that semantic concepts can affect the role of stimuli. Inspired by this observation, this paper presents a new MUlti-Level SEmantic Reasoning network (MULSER) for fine-grained image-text multimodal emotion classification, which not only investigates the semantic relationship among objects and words respectively, but also explores the semantic relationship between regional objects and global concepts. For image modality, we first build graphs to extract objects and global representation, and employ a graph attention module to perform bilevel semantic reasoning. Then, a joint visual graph is built to learn the regional-global semantic relations. For text modality, we build a word graph and further apply graph attention to reinforce the interdependencies among words in a sentence. Finally, a cross-modal attention fusion module is proposed to fuse semantic-enhanced visual and textual features, based on which informative multimodal representations are obtained for fine-grained emotion classification. The experimental results on public datasets demonstrate the superiority of the proposed model over the state-of-the-art methods.

In the heterogeneous vehicular networks (HetVNets), the roadside units (RUs) can exploit the massive amounts of valuable data collected by vehicles to complete federated learning tasks. However, most of the existing studies consider the scenario of one task requester (TR) and ignore the fact that multiple TRs may concurrently request their model training tasks in the HetVNets. In this paper, we consider the scenario of multi-TR and multi-RU and propose a digital twins (DT) enabled on-demand matching scheme for multi-task federated learning to address the two-way selection problem between TRs and RUs. Specifically, by jointly considering the diversified requirements of the TRs and the differentiated training capabilities of the RUs, we first design a DT enabled on-demand matching architecture to facilitate the multi-task federated learning in the HetVNets. Then, based on the personalized requirement of the DT of each TR (DT-TR), a marginal utility based vehicle selection mechanism is proposed to enable the DT of each RU (DT-RU) to determine the customized model training strategy. With the determined strategies, the two-way selection problem between the DT-TRs and the DT-RUs is formulated as an on-demand matching game in DT networks, where a matching algorithm is designed to obtain their optimal strategies. Simulation results demonstrate that the proposed scheme outperforms the conventional schemes in terms of training accuracy, performance-cost ratio (PCR), and task completion rate (TCR).