Large Language Models for Simulating Professions (SP-LLMs), particularly as teachers, are pivotal for personalized education. However, ensuring their professional competence and ethical safety remains a major challenge, as existing benchmarks fail to measure role-playing fidelity or address the unique teaching harms inherent in educational scenarios. To address this gap, we propose EduGuardBench, a dual-component benchmark that evaluates professional fidelity through the Role-playing Fidelity Score (RFS) and diagnoses harms specific to the teaching profession. It also probes safety vulnerabilities using persona-based adversarial prompts targeting both general harms and academic misconduct, with metrics such as Attack Success Rate (ASR) and a three-tier Refusal Quality assessment. Extensive experiments on 14 leading models reveal a stark polarization in performance. While reasoning-oriented models generally demonstrate higher fidelity, incompetence remains the dominant failure mode across most models. Adversarial testing uncovered a counterintuitive scaling paradox, where mid-sized models appear more vulnerable, challenging monotonic safety assumptions. Notably, we identify an Educational Transformation Effect, where the safest models convert harmful requests into teachable moments through ideal educational refusals. This ability is strongly negatively correlated with ASR, revealing a new dimension of advanced AI safety. EduGuardBench thus provides a reproducible framework for holistic assessment of professional, ethical, and pedagogical alignment, uncovering dynamics critical to deploying trustworthy AI in education.

Region representation learning plays a pivotal role in urban computing by extracting meaningful features from unlabeled urban data. Analogous to how perceived facial age reflects an individual's health, the visual appearance of a city serves as its "portrait", encapsulating latent socio-economic and environmental characteristics. Recent studies have explored leveraging Large Language Models (LLMs) to incorporate textual knowledge into imagery-based urban region representation learning. However, two major challenges remain: i) difficulty in aligning fine-grained visual features with long captions, and ii) suboptimal knowledge incorporation due to noise in LLM-generated captions. To address these issues, we propose a novel pre-training framework called UrbanLN that improves Urban region representation learning through Long-text awareness and Noise suppression. Specifically, we introduce an information-preserved stretching interpolation strategy that aligns long captions with fine-grained visual semantics in complex urban scenes. To effectively mine knowledge from LLM-generated captions and filter out noise, we propose a dual-level optimization strategy. At the data level, a multi-model collaboration pipeline automatically generates diverse and reliable captions without human intervention. At the model level, we employ a momentum-based self-distillation mechanism to generate stable pseudo-targets, facilitating robust cross-modal learning under noisy conditions. Extensive experiments across four real-world cities and various downstream tasks demonstrate the superior performance of our UrbanLN.

Accurate travel time estimation (TTE) plays a crucial role in intelligent transportation systems. However, it remains challenging due to heterogeneous data sources and complex traffic dynamics. Moreover, traditional approaches typically convert trajectory data into fixed-length representations. This overlooks the inherent variability of real-world motion patterns, often resulting in information loss and redundancy. To address these challenges, this paper introduces the Multimodal Dynamic Trajectory Integration (MDTI) framework--a novel multimodal trajectory representation learning approach that integrates GPS sequences, grid trajectories, and road network constraints to enhance the performance of TTE. MDTI employs modality-specific encoders and a multimodal fusion module to capture complementary spatial, temporal, and topological semantics, while a dynamic trajectory modeling mechanism adaptively regulates information density for trajectories of varying lengths. Two self-supervised pretraining objectives, named contrastive alignment and masked language modeling, further strengthen multimodal consistency and contextual understanding. Extensive experiments on three real-world datasets demonstrate that MDTI consistently outperforms state-of-the-art baselines, confirming its robustness and strong generalization abilities. The code is publicly available at: https://github.com/City-Computing/MDTI.

Cross-domain sequential recommendation is increasingly important in modern Web ecosystems, where user behaviors span multiple independently operated services that maintain strict data isolation for privacy and regulatory compliance. Federated learning offers a practical paradigm for such cross-domain collaboration, but user preferences evolve asynchronously across services, creating a substantial distribution shift. This drift leads to unstable and unequal domain contributions: behaviorally rich domains dominate global updates, while low-resource or volatile domains exert limited influence. Such an imbalance degrades recommendation accuracy and raises fundamental fairness concerns. To address these challenges, we propose FairFRL, a fairness-aware federated representation learning framework designed to mitigate contribution imbalance under dynamic cross-domain drift. FairFRL mitigates contribution imbalance under dynamic cross-domain drift by jointly regulating domain influence during federated aggregation and disentangling domain-shared and domain-exclusive semantics, while preserving data locality. Experiments on real-world Amazon multi-domain datasets show that FairFRL consistently outperforms strong federated and centralized baselines across multiple metrics and achieves more equitable cross-domain contributions. These results position FairFRL as a principled step toward responsible, fair, and socially aligned Web recommendation systems.

Cross-domain sequential recommendation (CDSR) plays a critical role in decentralized Web applications by leveraging user behavior sequences across multiple platforms to alleviate data sparsity and capture dynamic preferences. However, existing federated CDSR frameworks face two fundamental challenges: (i) heterogeneous sequential interactions that encode domain-exclusive semantics and cannot be directly shared under privacy constraints, and (ii) strong trust assumptions that both servers and clients behave honestly, leaving federated training vulnerable to misreporting, malicious updates, and negative transfer. In this paper, we propose VeriFRL, a verifiable federated representation learning framework for cross-domain sequential recommendation. VeriFRL adopts a dual-module design that integrates representation learning with verifiable training: an attention-based variational encoder disentangles domain-shared and domain-exclusive representations to support transferable and privacy-preserving knowledge sharing, while a contribution evaluation module quantifies client-level and feature-level influences to enable verifiability, interpretability, and negative transfer detection. Extensive experiments on real-world multi-domain datasets demonstrate that VeriFRL achieves competitive or superior recommendation performance over state-of-the-art federated CDSR methods, while providing fine-grained insights into cross-domain knowledge transfer dynamics.

Temporal Knowledge Graph (TKG) reasoning seeks to predict future events by analyzing historical data, where the effective leverage of both local and global historical facts proves crucial. Existing approaches employ graph neural networks (GNNs) and recurrent neural networks (RNNs) for local evolution patterns, complemented by statistical methods to enhance attention to global facts, demonstrating efficient predictive capabilities. However, traditional GNNs, constrained by their low-order neighborhood aggregation design, inherently fail to model potential high-order dependencies among facts. Furthermore, existing global history modeling approaches may introduce irrelevant historical information that interferes with prediction tasks. To address these limitations, we propose a Dual History-aware HyperGraph Network for TKG reasoning, namely DHHGN. Specifically, for local history modeling, we design a hybrid hypergraph-graph joint recurrent convolution module that simultaneously captures low-order neighborhood information and high-order interaction patterns among entities, employing a gating mechanism to adaptively blend their contributions. For global history modeling, we propose a dual history enhancement module that amplifies attention on pivotal historical facts while ensuring holistic integration of all historical contexts. Extensive experiments on four public benchmarks validate that DualHist-HGN consistently outperforms existing state-of-the-art methods across TKG reasoning tasks.

Temporal Knowledge Graph Completion (TKGC) aims to predict missing entities or relations based on historical facts, thereby facilitating the understanding of dynamic system evolution and supporting downstream reasoning tasks. However, existing methods predominantly focus on modeling sequential and structural dependencies, often overlooking the rich semantic information embedded in entities and relations, as well as the higher-order interactions among them, which limits their ability to handle complex, evolving scenarios effectively. To address these limitations, we propose DSTAG, a novel TKGC approach based on a semantic tag-enhanced dual-graph convolutional network. Our method leverages large language models to generate contextualized semantic multi-tags for both entities and relations (e.g., ''political event,'' ''economic activity''), thereby enriching their semantic representations. Furthermore, we introduce a semantic tag representation mechanism that captures higher-order dependencies during the aggregation and propagation of semantic tag information across graphs. DSTAG adopts a dual-graph convolutional network architecture, where the relation graph convolution extracts semantic features between temporal relationships and injects this information into the entity graph convolution, enabling joint modeling of entities and relations. We evaluate DSTAG on three widely used TKG benchmarks: ICEWS14, ICEWS18, and ICEWS05-15. Experimental results show that DSTAG achieves substantial MRR improvements over state-of-the-art baselines by 8.64%, 9.81% and 4.56%, respectively.

Real-time traffic forecasting acts as a critical enabling service for IoT-driven Intelligent Transportation Systems (ITS). While existing Spatiotemporal Graph Neural Networks (STGNNs) achieve superior forecasting accuracy, their intensive computational complexity and high latency create a deployment bottleneck for resource-constrained IoT edge devices. To address this resource-accuracy mismatch, we propose a novel framework termed Dynamic Hub-Aware Knowledge Distillation (DHKD). Unlike traditional uniform distillation paradigms, DHKD introduces a topology-aware strategy to transfer knowledge from a complex teacher to a lightweight Spatiotemporal Multi-Layer Perceptron (STMLP) student model. Specifically, we design a dynamic hub-aware gating (DHAG) mechanism that adaptively identifies time-varying pivotal sensing nodes (hubs), ensuring that the student model prioritizes the most information-dense spatiotemporal regions. Furthermore, we develop a multi-level distillation strategy that aligns both intermediate features and final predictions through contrastive learning and attention mechanisms. Extensive experiments conducted on four real-world datasets demonstrate that DHKD significantly reduces inference latency while maintaining state-of-the-art performance, validating its viability for deployment on resource-constrained Internet of Vehicles (IoV) edge devices. The implementation code is available at https://anonymous.4open.science/r/DHKD.

The Transformer model, with its ability to capture long-term dependencies, has demonstrated significant potential in enhancing long-term traffic flow prediction for effective urban transportation management. However, most existing Transformer-based methods adhere to a centralized approach, failing to address privacy concerns and to optimize computational resource utilization. Although emerging federated learning paradigms offer privacy protection, the average aggregation still overlooks client heterogeneity and lacks the synchronous efficiency required for traffic flow prediction tasks. Consequently, we introduce FedTFormer, a hierarchical federated learning framework tailored to boost the performance of Transformer models in decentralized environments, involving clients, edge servers, and a central server. Initially, clients are organized into clusters through a sophisticated static clustering mechanism anchored in bipartite graph theory. FedTFormer enhances robustness of Transformer by facilitating synchronous average aggregation within clusters. Additionally, it performs asynchronous fine-tuning of cluster-specific parameters, leveraging hypernetwork constructed on the central server. Clients utilize an optimized Transformer model for localized training, harnessing its proficiency in capturing long-term spatio-temporal dependencies. Ultimately, we conduct extensive experiments across three datasets, comparing our method against ten sophisticated approaches and demonstrating the effectiveness and robustness of FedTFormer.

Spatiotemporal graph modeling aims to model temporally varying and spatially correlated information sequences in the traffic network, serving as a significant approach to capture the complex, dynamic, and nonlinear changes of traffic systems. Consequently, it plays a crucial role in enhancing the reliability of intelligent transportation systems. Existing methods extract traffic data’s features by designing sophisticated spatiotemporal graph neural network models, and have reached a bottleneck. Furthermore, these spatiotemporal graph models adjust their parameters globally across different datasets, limiting their generalizability and universality. Meanwhile, the pre-training and fine-tuning paradigm has greatly progressed in natural language processing. This paper proposes a Generative Adversarial Pre-trained Spatial-Temporal Graph Convolutional Network (GAPSTGCN) to break through the bottleneck of spatiotemporal modeling of traffic data. Specifically, by developing a spatiotemporal graph encoder that incorporates domain knowledge about traffic system behavior and network science. The module can extract the dynamic change trend of data from the fusion process of spatiotemporal features, while introducing a time-gating mechanism that enhances its effectiveness in medium and long-term predictions. Moreover, we embed the encoding network into the generator and discriminator. Subsequently, we employ the concept of adversarial data generation to pre-train the spatiotemporal graph neural networks, enhancing the model’s knowledge emergence capabilities. Besides, we design an adapter for downstream task prediction to bridge the gap between pre-training and prediction tasks, aligning pre-trained features with downstream task features. Extensive experiments indicate that our model outperforms the state-of-the-art on the datasets. Experiments conducted on various types of traffic datasets further demonstrate the model’s generality.

The rapid advancement of Internet of Vehicles technology has led to massive growth in vehicular data generation, imposing strict computational demands for latency-sensitive applications. By offloading computational tasks to Road-side Units (RSUs), Vehicular Edge Computing (VEC) offers an efficient solution for those latency-sensitive applications. However, current task offloading schemes generally ignore the time-varying topology caused by vehicle mobility, which poses a risk of task interruption. Moreover, existing task offloading models primarily focus on serial tasks processing and fail to adequately account for the relationships among parallel tasks, leading to inefficient resource utilization and potential latency accumulation in multi-task VEC scenarios. To this end, we propose a mobility-awareCollaborativeTaskOffloading scheme forParallel tasks (CoTOP) in VEC. Integrated with vehicle mobility detection, a collaborative task offloading model is designed based on deep reinforcement learning, achieving effective coordination among RSUs to reduce task processing latency. Additionally, a task prioritization algorithm is incorporated to optimize resource allocation. Experimental results show that CoTOP significantly outperforms existing schemes in terms of task processing latency, energy consumption and completion ratio.

Edge intelligence capitalizes on heterogeneous edge resources to empower artificial intelligence–driven applications while mitigating dependence on centralized cloud infrastructures. In mobile edge computing (MEC) systems, resource scheduling constitutes a latency-critical task within inherently dynamic environments. Deep reinforcement learning (DRL) has been widely employed to address this challenge, as it enables agents to derive task-specific policies through continual interactions with the environment. Nevertheless, MEC environments exhibit substantial heterogeneity, and the relative importance of various resource types may fluctuate as the system evolves, thereby altering task objectives. Consequently, agents often necessitate retraining across new domains, the process that is computation ally intensive and time inefficient. To surmount these challenges, transfer reinforcement learning (TRL) has emerged as a promising paradigm that facilitates the transfer of agents' acquired knowledge from the source domain to the target domain. To the best of our knowledge, this is the first comprehensive survey dedicated to TRL applications in MEC systems. Specifically, we first elucidates the foundational principles of TRL, encompassing problem formulation and DRL methodologies. Subsequently, we systematically reviews and critically analyzes existing research on TRL and its applications in MEC systems. Finally, we discuss persistent technical limitations and delineates promising avenues for future exploration.

As urban residents demand higher travel quality, vehicle dispatch has become a critical component of online ride-hailing services. However, current vehicle dispatch systems struggle to navigate the complexities of urban traffic dynamics, including unpredictable traffic conditions, diverse driver behaviors, and fluctuating supply and demand patterns. These challenges have resulted in travel difficulties for passengers in certain areas, while many drivers in other areas are unable to secure orders, leading to a decline in the overall quality of urban transportation services. To address these issues, this paper introduces GARLIC: a framework of GPT-Augmented Reinforcement Learning with Intelligent Control for vehicle dispatching. GARLIC utilizes multiview graphs to capture hierarchical traffic states, and learns a dynamic reward function that accounts for individual driving behaviors. The framework further integrates a GPT model trained with a custom loss function to enable high-precision predictions and optimize dispatching policies in real-world scenarios. Experiments conducted on two real-world datasets demonstrate that GARLIC effectively aligns with driver behaviors while reducing the empty load rate of vehicles.

Federated Graph Learning (FGL) has emerged as a key paradigm for distributed graph machine learning, enabling cross-domain graph collaborative modeling while preserving data privacy. However, existing methods face two major bottlenecks: the structural heterogeneity discrepancy of graph data among clients weakens the generalization ability of the global model; and model heterogeneity leads to inefficient knowledge sharing and complex global aggregation. To address these issues, we propose FedGVD, an efficient framework that constructs a global perspective through data condensation and server-side virtual node generation, which not only preserves the semantic equivalence of the original data but also avoids privacy leakage. Subsequently, by distributing low-dimensional generalizable knowledge for unidirectional distillation, FedGVD enables local models to absorb global knowledge without transmitting local parameters, thus breaking through the challenges of data and structural heterogeneity as well as model heterogeneity. This innovative approach ensures privacy-preserving and efficient federated graph collaboration. Experiments show that FedGVD maintains excellent performance in heterogeneous model scenarios while significantly improving communication efficiency, offering a new approach for privacy-preserving collaborative modeling in FGL. The code is available at https://github.com/Jasonxx4/FedGVD.

Video action recognition involves interpreting both global context and specific details to accurately identify actions. While previous models are effective at capturing spatiotemporal features, they often lack a focused representation of key action details. To address this, we introduce FocusVideo, a framework designed for refining video action recognition through integrated global and local feature learning. Inspired by human visual cognition theory, our approach balances the focus on both broad contextual changes and action-specific details, minimizing the influence of irrelevant background noise. We first employ learnable action queries to selectively emphasize action-relevant regions without requiring region-specific labels. Next, these queries are learned by a local action streaming branch that enables progressive query propagation. Moreover, we introduce a parameter-free feature interaction mechanism for effective multi-scale interaction between global and local features with minimal additional overhead. Extensive experiments demonstrate that FocusVideo achieves state-of-the-art performance across multiple action recognition datasets, validating its effectiveness and robustness in handling action-relevant details.

Simulating Professions (SP) enables Large Language Models (LLMs) to emulate professional roles.However, comprehensive psychological and ethical evaluation in these contexts remains lacking.This paper introduces EMNLP, an Educator-role Moral and Normative LLMs Profiling framework for personality profiling, moral development stage measurement, and ethical risk under soft prompt injection.EMNLP extends existing scales and constructs 88 teacher-specific moral dilemmas, enabling profession-oriented comparison with human teachers.A targeted soft prompt injection set evaluates compliance and vulnerability in teacher SP.Experiments on 14 LLMs show teacher-role LLMs exhibit more idealized and polarized personalities than human teachers, excel in abstract moral reasoning, but struggle with emotionally complex situations.Models with stronger reasoning are more vulnerable to harmful prompt injection, revealing a paradox between capability and safety.The model temperature and other hyperparameters have limited influence except in some risk behaviors.This paper presents the first benchmark to assess ethical and psychological alignment of teacher-role LLMs for educational AI.Resources are available at https://e-m-n-l-p.github.io/.

Knowledge graphs (KGs) have been widely adopted to mitigate data sparsity and address cold-start issues in recommender systems. While existing KGs-based recommendation methods can predict user preferences and demands, they fall short in generating explicit recommendation paths and lack explainability. As a step beyond the above methods, recent advancements utilize reinforcement learning (RL) to find suitable items for a given user via explainable recommendation paths. However, the performance of these solutions is still limited by the following two points. (1) Lack of ability to capture contextual dependencies from neighboring information. (2) The excessive reliance on short recommendation paths due to efficiency concerns. To surmount these challenges, we propose a category-aware dual-agent reinforcement learning (CADRL) model for explainable recommendations over KGs. Specifically, our model comprises two components: (1) a category-aware gated graph neural network that jointly captures context-aware item representations from neighboring entities and categories, and (2) a dual-agent RL framework where two agents efficiently traverse long paths to search for suitable items. Finally, experimental results show that CADRL outperforms state-of-the-art models in terms of both effectiveness and efficiency on large-scale datasets.

Point-of-Interest (POI) recommendation is a critical task in location-based services, which seeks to suggest a user's next potential location by leveraging their check-in history. However, the accuracy of such data is frequently compromised by noise, arising from factors such as inadvertent check-ins and the influence of recommendation biases, which hampers the accurate modeling of user preferences. To mitigate the impact of noisy implicit feedback, we propose the Diffusion-Based Implicit Feedback Modeling for Point-of-Interest Recommendation (DIPOI). First, a hybrid hypergraph convolutional network is utilized to perform multi-hop relational reasoning, enabling the discovery of deep-seated preferences, particularly for users with low activity levels. Subsequently, DIPOI integrates the spatial learning capacity of graph neural networks with the temporal modelling strengths of attention mechanisms. This integration enables the capture of the structural dependencies among POIs and the sequential dynamics of user behaviour. Finally, a diffusion model is incorporated to progressively denoise the latent representation space via a reverse generative process based on users' historical travel preferences, thereby increasing the fidelity of the implicit feedback modelling. Experiments conducted on three real-world datasets show that DIPOI consistently achieves higher recommendation accuracy than state-of-the-art baselines, validating its effectiveness and robustness. The implementation code is available at https://github.com/dengzhaolin/DIPOI.

Recommendation systems can establish vital connections between users and information. But they are still facing data sparsity and cold start issues. Cross-domain recommendation improves performance via knowledge transfer, yet often depends on data sharing, conflicting with privacy needs. Federated learning offers a privacy-preserving alternative, but still struggles with knowledge transfer and communication overhead. In this paper, we propose FedPce, a Prompt-based Federated Cross-domain Recommendation. FedPce improves cross-domain recommendation and promotes knowledge transfer by leveraging item description texts and prompt learning, while reducing communication overhead and accelerating convergence through low-rank gradient compression and elastic aggregation strategies. Experimental results show that FedPce performs well on multidomain recommendation tasks, balancing recommendation effect and privacy protection.

Large language models have demonstrated remarkable generalization capabilities and strong performance across various fields. Recent research has highlighted their significant potential in time series forecasting. However, time series data often exhibit complex periodic characteristics, posing a substantial challenge in enabling these models to effectively capture latent patterns. To address this challenge, we propose a novel framework, LLM-TPF, which leverages individuality and commonality fusion to enhance time series forecasting. In the frequency domain, periodic features are extracted to reveal the intrinsic periodicity of the data, while textual prototypes are used to indicate temporal trends. In the time domain, carefully designed prompts are employed to guide the models in comprehending global information. A commonality fusion mechanism further aggregates heterogeneous information across dimensions, and three distinct language models are utilized to independently process different types of information. Extensive real-world experiments demonstrate that LLM-TPF is a powerful tool for time series forecasting, achieving superior performance compared to state-of-the-art specialized models and exhibiting exceptional generalization ability in zero-shot scenarios. Code is available at https://github.com/switchsky/LLM-TPF.

Spatio-temporal graph modeling is widely applied to spatio-temporal data, analyzing the relationships between data to achieve accurate predictions. However, despite the excellent predictive performance of increasingly complex models, their intricate architectures result in significant memory overhead and computational complexity when handling spatio-temporal data, which limits their practical applications. To address these challenges, we propose a plug-and-play SubGraph Learning (SGL) method to reduce the memory overhead without compromising performance. Specifically, we introduce a SubGraph Partition Module (SGPM), which leverages a set of learnable memory vectors to select node groups with similar features from the graph, effectively partitioning the graph into smaller subgraphs. Noting that partitioning the graph may lead to feature redundancy, as overlapping information across subgraphs can occur. To overcome this, we design a SubGraph Feature Aggregation Module (SGFAM), which mitigates redundancy by averaging node features from different subgraphs. Experiments on four traffic network datasets of various scales demonstrate that SGL can significantly reduce memory overhead, achieving up to a 56.4\% reduction in average GPU memory overhead, while maintaining robust prediction performance. The source code is available at https://github.com/wengwenchao123/SubGraph-Learning.

Out-of-town trip recommendation aims to generate a sequence of Points of Interest (POIs) for users traveling from their hometowns to previously unvisited regions based on personalized itineraries, e.g., origin, destination, and trip duration. Modeling the complex user preferences--which often exhibit a two-fold nature of static and dynamic interests--is critical for effective recommendations. However, the sparsity of out-of-town check-in data presents significant challenges in capturing such user preferences. Meanwhile, existing methods often conflate the static and dynamic preferences, resulting in suboptimal performance. In this paper, we for the first time systematically study the problem of out-of-town trip recommendation. A novel framework SPOT-Trip is proposed to explicitly learns the dual static-dynamic user preferences. Specifically, to handle scarce data, we construct a POI attribute knowledge graph to enrich the semantic modeling of users’ hometown and out-of-town check-ins, enabling the static preference modeling through attribute relation-aware aggregation. Then, we employ neural ordinary differential equations (ODEs) to capture the continuous evolution of latent dynamic user preferences and innovatively combine a temporal point process to describe the instantaneous probability of each preference behavior. Further, a static-dynamic fusion module is proposed to merge the learned static and dynamic user preferences. Extensive experiments on real data offer insight into the effectiveness of the proposed solutions, showing that SPOT-Trip achieves performance improvement by up to 17.01%.

Temporal knowledge graph completion (TKGC) seeks to develop more comprehensive knowledge representations by addressing missing relationships and entities within temporal knowledge graphs (TKGs), thereby enhancing reasoning and predictive capabilities in downstream tasks. Nonetheless, real-world knowledge-such as the progression of social network interactions and the unfolding of news events-is inherently dynamic, resulting in substantial sparsity issues in TKGs that profoundly impair the performance of TKGC models. To overcome this challenge, we introduce the Adaptive Neighborhood Enhancement Layer (ANEL), a novel module that can be effortlessly integrated into existing TKGC models to substantially elevate the representation quality of sparse entities. ANEL first derives initial entity embeddings through a base model and then uncovers concealed semantic relationships between entities via a latent relation module, enriching the explicit relationships within the knowledge graph. Furthermore, ANEL incorporates an adaptive latent information adjustment component, which dynamically calibrates the influence of latent information based on the entity's relational structure: entities with fewer connections derive greater benefit from latent information, while entities with denser connections become less dependent on latent augmentation, ensuring precise and resilient representations. We conducted comprehensive experiments on four prominent benchmark datasets, and the results underscore the effectiveness and superiority of ANEL in TKGC tasks.

Abstract Trajectories serve as a cornerstone of intelligent transportation systems, playing an important role in many applications such as traffic flow prediction, route planning, and urban management. However, the availability of such data is limited due to privacy issues, ethical concerns, and the high cost associated with infrastructure deployment. In recent years, rapidly developing generative models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Diffusion Models (DMs) have demonstrated strong capabilities in learning complex data distributions and generating synthetic data, thereby alleviating the data accessibility issue. In this survey, we systematically review the existing literature on deep generative models that address the problem of trajectory generation. First, we classify the existing literature into two categories: unconditional and conditional trajectory generation. In unconditional generation, trajectories are generated without contextual constraints, whereas conditional generation incorporates several important factors such as road network topology, time of day, and user preferences to guide the trajectory generation process. Then, for each category, we further classify the literature into three methodological types, including VAEs, GANs, and DMs, and analyze how these models address key challenges under different settings. Finally, we discuss promising directions for future research and hope to inspire further advances in trajectory generation.

Artificial Intelligence Generated Content (AIGC) has rapidly emerged with the capability to generate different forms of content, including text, images, videos, and other modalities, which can achieve a quality similar to content created by humans. As a result, AIGC is now widely applied across various domains such as digital marketing, education, and public health, and has shown promising results by enhancing content creation efficiency and improving information delivery. However, there are few studies that explore the latest progress and emerging challenges of AIGC across different domains. To bridge this gap, this paper brings together 16 scholars from multiple disciplines to provide a cross-domain perspective on the trends and challenges of AIGC. Specifically, the contributions of this paper are threefold: (1) It first provides a broader overview of AIGC, spanning the training techniques of Generative AI, detection methods, and both the spread and use of AI-generated content across digital platforms. (2) It then introduces the societal impacts of AIGC across diverse domains, along with a review of existing methods employed in these contexts. (3) Finally, it discusses the key technical challenges and presents research propositions to guide future work. Through these contributions, this vision paper seeks to offer readers a cross-domain perspective on AIGC, providing insights into its current research trends, ongoing challenges, and future directions.

Due to the robust representational capabilities of graph data, employing graph neural networks for its processing has demonstrated superior performance over conventional deep learning algorithms. Graph data encompasses abundant features and structural information; however, its large-scale collection is often challenging in practice. This difficulty arises because data predominantly exists in isolated compartments, making it arduous to harmonize information across various organizations or to enable multiple organizations to collaborate effectively while safeguarding local data privacy. In light of an extreme data distribution scenario, where each client possesses distinct nodes with partially overlapping segments yet divergent data features, we introduce a dual-cloud server architecture. This framework encompasses the design of four secure subprotocols: ReEnc (secure re-encryption), SecPSI (secure outsourcing of PSI), SecWeight (secure weight calculation), and SecAgg (secure aggregation). Together, these components facilitate a vertical federated learning framework for graph convolutional networks, ensuring privacy preservation. We provide a security proof for the entire system and extensive evaluation on three benchmark datasets (Cora, Citeseer, and Pubmed) illustrates that our Vertical Federated Graph Convolutional Network (VFGCN) surpasses existing privacy-preserving methodologies.

In recent years, deep learning has increasingly gained attention in the field of traffic prediction. Existing traffic prediction models often rely on GCNs or attention mechanisms withO(N2) complexity to dynamically extract traffic node features, which lack efficiency and are not lightweight. Additionally, these models typically only utilize historical data for prediction, without considering the impact of the target information on the prediction. To address these issues, we propose a Pattern-Matching Dynamic Memory Network (PM-DMNet). Unlike traditional attention and graph convolution-based approaches, PM-DMNet employs a novel dynamic memory network that stores the most representative traffic patterns from historical data in a memory matrix through training. It captures traffic pattern features by comparing the similarity between the memory matrix and the current traffic state. This method not only achieves excellent predictive performance but also significantly reduces computational complexity toO(N). The PM-DMNet also introduces two prediction methods: Recursive Multi-step Prediction (RMP) and Parallel Multi-step Prediction (PMP), which leverage the time features of the prediction targets to assist in the prediction process. Furthermore, a transfer attention mechanism is integrated into PMP, transforming historical data features to better align with the predicted target states, thereby capturing trend changes more accurately and reducing errors. Extensive experiments demonstrate the superiority of the proposed model over existing benchmarks. The source codes are available at: https://github.com/wengwenchao123/PM-DMNet

Missing traffic data is a common phenomenon in practice, which has serious implications for the development of downstream data-driven traffic research. Although numerous methods have been proposed to impute missing values, two limitations still exist: lack of complete ground truth and reliance on carefully constructed model structures. In this paper, we propose a novel deep neural imputation network framework named STIseq2seq, based on self-supervised contrastive learning. It follows a sequence-to-sequence architecture with attention mechanism to address the traffic data imputation problem. Specifically, the model consists of two key components: encoder and decoder, both composed of ST-block. Initially, we utilize dynamic graph convolution (DGC) and gated recurrent unit (GRU) to construct the fundamental unit, ST-block, for learning spatiotemporal dependencies. Then, a bidirectional ST-block is employed to model incomplete input data in the encoder; while a forward ST-block combined with an attention mechanism is used to accurately impute missing data in the decoder. Lastly, we devise a training method based on self-supervised contrastive learning, achieved through randomly shuffling the masking matrix, to enhance training effectiveness. Extensive experiments conducted on two available real-world traffic datasets demonstrate that our proposed STIseq2seq has superior and stable performance compared to multiple baseline models under different missing data scenarios with missing rates ranging from 20% to 80%. Visualization analysis of the imputation results further provides insights into the advantages of STIseq2seq.

The primary objective of graph node anomaly detection is to pinpoint rare patterns that display marked deviations from the typical one. Existing methods utilize Graph Convolutional Networks (GCNs) to model complex interactions in Heterogeneous Information Networks (HINs), typically homogenizing HINs using meta-paths to effectively focus on particular semantic scenarios. However, meta-paths excessively emphasize specific nodes and their connections on predefined paths, leading to the neglect of one-hop context-rich neighbors. Furthermore, the conversion from heterogeneous to homogeneous structures disrupts inherent relationships, resulting in an irreversible loss of direct links. Thus, we propose a dual-view-based H eterogeneous I nformation N etworks Node Ano maly Detection framework, HINAno, to mitigate structural loss. HINAno adopts a synergetic approach that balances local structural information with semantic richness, drawing from both the one-hop neighbor view and the meta-path view. Specifically, this dual-view utilizes hierarchical fusion mechanisms at node, type, and semantic levels to capture one-hop and multi-hop neighborhoods in a level-wise manner. In addition, HINAno adopts self-supervised contrastive learning and GCNs to amplify the gap between normal and abnormal nodes, thereby reducing the reliance on anomalous labels and enhancing the capability of anomaly detection. Finally, we successfully verify that the HINAno framework is effective and superior on four real-world datasets.

Attribute graph anomaly detection aims to identify nodes that significantly deviate from the majority of normal nodes, and has received increasing attention due to the ubiquity and complexity of graph-structured data in various real-world scenarios. However, current mainstream anomaly detection methods are primarily designed for centralized settings, which may pose privacy leakage risks in certain sensitive situations. Although federated graph learning offers a promising solution by enabling collaborative model training in distributed systems while preserving data privacy, a practical challenge arises as each client typically possesses a limited amount of graph data. Consequently, naively applying federated graph learning directly to anomaly detection tasks in distributed environments may lead to suboptimal performance results. We propose a federated graph anomaly detection framework via contrastive self-supervised learning (CSSL) [federated CSSL anomaly detection framework (FedCAD)] to address these challenges. FedCAD updates anomaly node information between clients via federated learning (FL) interactions. First, FedCAD uses pseudo-label discovery to determine the anomaly node of the client preliminarily. Second, FedCAD employs a local anomaly neighbor embedding aggregation strategy. This strategy enables the current client to aggregate the neighbor embeddings of anomaly nodes from other clients, thereby amplifying the distinction between anomaly nodes and their neighbor nodes. Doing so effectively sharpens the contrast between positive and negative instance pairs within contrastive learning, thus enhancing the efficacy and precision of anomaly detection through such a learning paradigm. Finally, the efficiency of FedCAD is demonstrated by experimental results on four real graph datasets.

Multivariate time series anomaly detection plays a crucial role in industrial production. However, the inherent complexity and randomness of time series pose significant challenges. Furthermore, existing detection methods struggle to provide reliable explanations for outliers. To address these issues, this paper presents an unsupervised multivariate time series anomaly detection model named Point-Correlate Adversarial Transformer (PCAT). In this work, we leverage Transformer networks to capture the underlying correlations between different points in a time series and reconstruct the original sequence. By analyzing the correlation differences and reconstruction errors, we identify anomalies at the point level. Our model incorporates an adversarial structure, enabling unsupervised learning and enhancing the learning capability and robustness of the detection network. Experimental evaluations on four real-world datasets demonstrate the superiority of our approach over other state-of-the-art models in terms of detection delay and accuracy.

Knowledge graph completion (KGC) is dedicated to deducing absent facts within incomplete knowledge graphs (KGs). The majority of antecedent studies exclusively address the transductive scenario, wherein all entities are observed throughout the training process, which is impractical for the everyday reality with constantly emerging entities. Recent works that concern this issue mainly focus on representing emerging entities with seen neighbors while ignoring the semantics in the queried relations. In this paper, we introduce a model named DKGC, which incorporates two purposefully designed entity encoding modules to capture the local and global entity representations in a relation-specific manner and a sampling module with a diffusion process to explore the potential semantics further. Extensive experiments on benchmark datasets indicate the superiority of our model for inductive KGC.

Unmanned Aerial Vehicles (UAVs) can capture pictures of road conditions in all directions and from different angles by carrying high-definition cameras, which helps gather relevant road data more effectively. However, due to their limited energy capacity, drones face challenges in performing related tasks for an extended period. Therefore, a crucial concern is how to plan the path of UAVs and minimize energy consumption. To address this problem, we propose a multi-agent deep deterministic policy gradient-based (MADDPG) algorithm for UAV path planning (MAUP). Considering the energy consumption and memory usage of MAUP, we have conducted optimizations to reduce consumption on both fronts. Firstly, we define an optimization problem aimed at reducing UAV energy consumption. Secondly, we transform the defined optimization problem into a reinforcement learning problem and design MAUP to solve it. Finally, we optimize energy consumption and memory usage by reducing the number of neurons in the hidden layer of MAUP and conducting fine-grained pruning on connections. The final simulation results demonstrate that our method effectively reduces the energy consumption of UAVs compared to other methods.

The growing number of cars on city roads has led to an increase in traffic accidents, highlighting the need for traffic safety measures. Mobile trajectory anomaly detection is an important area of research that can identify unusual patterns or trajectories in urban environments and provide timely warnings to drivers to avoid accidents. However, there is a significant lack of research on the analysis of vehicle trajectory anomalies. To address this gap, we provide a comprehensive review of currently published papers on anomalous trajectories, highlighting important research trends and future directions. Besides, we innovatively classify trajectory anomalies into vehicle-based anomalies and driver-based anomalies according to whether they are caused by the driver’s behavior or not. The study further examines the existing challenges associated with analyzing anomalous trajectories and assesses the currently available solutions.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged as a valuable supplement to conventional MEC, offering unique advantages in temporary or emergency scenarios. However, the integration of UAVs into MEC introduces new challenges. First, the existing UAV-empowered MEC frameworks predominantly have not accounted for dependencies between the computing tasks. Second, the limited coverage area of UAVs and the sparse distribution of end devices result in a scarcity of training samples, hindering the effectiveness of data-driven approaches. To address these challenges, we introduce a novel UAV-assisted task offloading scheme designed to minimize the average task execution delay and energy consumption in vehicular networks. Initially, we craft a dependency-aware UAV-assisted MEC framework, where the task dependency and priority models are meticulously developed to illustrate the associations between the tasks. Subsequently, we devise an asynchronous federated deep reinforcement learning-based task offloading algorithm, incorporating an asynchronous federated optimization mechanism to enhance the data diversity, with a premise of ensuring data privacy. Our method has been rigorously tested using real traffic flow data and a directed acyclic graph task data set. Comparative experiments highlight the superiority of our framework, showcasing a substantial reduction in the average task delay and energy consumption.

The next point-of-interest (POI) recommendation aims to predict users’ future movements based on their historical trajectories. However, in reality, users may provide uncertain check-in records, resulting in uploaded data that lack precise location information and is instead ambiguous. Despite this challenge, only a limited number of studies have addressed this issue, often overlooking the intricate interactions among users, POIs, and POI categories. To that end, we propose a novel model called knowledge-graph-enhanced transformer (KGNext). KGNext leverages transition and interaction graphs derived from our constructed transitional-interactive knowledge graph (TIKG) to uncover both general movement patterns and varied user preferences regarding POIs and POI categories. Furthermore, KGNext integrates comprehensive contextual information from historical trajectories with TIKG to generate user trajectory embeddings. These encoded features are then utilized by a transformer model to provide fine-grained predictions of the next POI. Experimental results on three real-world datasets demonstrate the superiority of KGNext.

Urban overtourism results in heavy traffic, degraded tourist experiences, and overloaded infrastructure. Detecting urban overtourism at the early stage is important to minimize the adverse effects. However, urban overtourism detection (UOD) is a challenging task due to ambiguity, sparsity, and complex spatiotemporal relations of overtourism. In this article, we propose a novel UOD framework based on graph temporal convolutional networks (TCNs) to tackle the challenges mentioned above. More specifically, we propose the grid overtourism mode (GOM) to detect urban overtourism on a grid level and propose the overtourism detection mechanism, which gives a quantitative definition of overtourism and screens out the regions where overtourism may occur as candidate regions. Then, we construct the GOM graphs of the candidate regions. Next, we employ the graph TCNs to model the complex spatiotemporal relations of urban overtourism and predict the future GOM graph at the next time interval. Finally, we calculate the urban overtourism scores based on the prediction results. The experiments are conducted based on a real-world dataset. The evaluation results demonstrate the effectiveness of our methods.

Vehicle driving behavior analysis and detection tasks have become an indispensable part of intelligent transportation systems. Accurate pattern recognition of potential anomalies during the movement of entities is crucial for improving transportation efficiency. Current methods typically analyze vehicle trajectories independently without considering potential interactions among vehicles. To address this limitation, some studies have integrated graph attention mechanisms to capture the influence of neighboring vehicles during the aggregation process. However, Graph Attention Networks (GATs) are constrained by the univariate nature of attention heads and coefficients, thus lacking flexibility. In this work, we not only consider the social dynamics among neighboring vehicles but also delve into the limitations of GAT models. We propose a Vehicular Social Dynamics Anomaly Detection (VSD-AD) model based on the Recurrent Multi-Mask Aggregator (MMA) enabled Variational AutoEncoder (VAE) architecture to maximize the learning of relational embeddings among neighbors in a highway vehicle network. Furthermore, we apply Node Feature Quantisation (NFQ) to the encoder output to mitigate the complexity of neighbor relationships. Our model is flexible and customizable for different highway scenarios, suitable for large-scale highway vehicle video data. To validate real-world applicability, we further assess its performance on both the simulated dataset and real-world traffic dataset, where our model outperforms other mainstream methods in terms of detection performance.

The spatio-temporal prediction task in the transportation network is the core of the solutions for various traffic problems. On one hand, the mobility pattern in traffic can be reflected in the travel behavior of the crowd. In most traffic prediction tasks, the importance of the mobility pattern is often overlooked. On the other hand, traffic prediction also has a variety of predicting scenarios, including short-term and long-term prediction, and relevant research cannot solve the problems under the two scenarios at the same time. In view of the problem of existing work, we propose a multi-pattern traffic prediction framework, MPGNNFormer. First, we construct a new bus stop distance network to model the relationships between stops. Then, we use a graph neural network-based deep clustering method to extract the bus stop mobility pattern. Finally, we design a transformer-based spatio-temporal prediction model (STGNNFormer) to predict bus stop flow by taking full advantage of time dependency and space dependency. After that, we conduct a series of experiments to evaluate and test them on the real bus dataset, including analyzing mobility patterns and comparing prediction results. The experimental results prove that MPGNNFormer can improve the calculation efficiency in the prediction scene while ensuring prediction accuracy in the stop-flow prediction of the transportation network.

Intelligent Transportation Systems (ITS) are essential and play a key role in improving road safety, reducing congestion, optimizing traffic flow and facilitating the development of smart cities. The collection of data from ITS and its transformation into information is challenged by the presence of missing data in datasets. Timely and effective handling of missing data is crucial to facilitate intelligent decision making. In response to this need, numerous researchers have proposed various techniques for dealing with missing data, with commendable results in different scenarios. Therefore, this survey aims to provide a well-organized and thorough overview of the research related to imputation of missing data in traffic. Our purposes are at least fourfold. First, we discuss the background of missing data and highlight the value of traffic data imputation. Second, we present a comprehensive list of missing patterns, open data, widely used evaluation metrics and performance goals for this issue. Third, we categorize related studies into three parts: interpolation-based, statistical learning-based, prediction-based methods, and provide a secondary classification within each category to better understand the characteristics and limitations of each method. Finally, we identify future research directions to advance the understanding of traffic data imputation.

The rapid development of technology such as virtual reality and augmented reality, coupled with the reduced direct contact due to the COVID-19 pandemic, has led to the emergence of a more advanced mode of interaction: air handwriting. This new form of human-computer interaction allows users to input text by writing in the air freely. However, deploying and applying existing air handwriting recognition systems in real-world scenarios still presents challenges, particularly in real-time performance, privacy protection, and label scarcity. To address these challenges, we propose a federated active learning framework called FedAWR for air handwriting recognition tasks. FedAWR utilizes distributed learning to train a shared global model in the cloud from multiple user devices at the network's edge, while keeping the user's handwritten data local to ensure privacy. In addition, FedAWR employs an interactive active learning strategy to collect user-provided annotations for iterative training during the online federated learning process, bootstrapping personalized models for each client. To further enhance interactivity and real-time performance, we designed a lightweight recognition model, which is integrated into FedAWR. Finally, extensive experiments were conducted on real-world air handwritten datasets to validate the superiority of FedAWR.

Mobile Crowd Sensing (MCS) represents a novel paradigm which utilizes intelligent devices carried by mobile users to collect and transmit data. Appropriate incentives are essential to recruit enough participants for sensing tasks. Existing works have designed some incentive mechanisms for MCS, which are not suitable for scenarios when the participants increase significantly as a result of the booming cost. To solve the above cost problem, a Utility-Maximization Incentive Mechanism (UMIM) is proposed in this paper by leveraging the influence propagation on the social network. Participants in the same social network can benefit from the data shared by others, which shows the utility of sensing data and can be regarded as a non-monetary incentive and make the participants stay positive under relative low payoff. Therefore, by improving the utility of sensing data, the incentive cost can be effectively reduced. To maximize the data utility, we further design a tree-based structure to improve the priority experience replay mechanism of Proximal Policy Optimization (PPO) in UMIM. This improvement makes the high priority experience to be sampled more quickly and efficiently, as a result, the network can learn more effectively. Numerical results show that UMIM can further improve the data utility and have better convergence.

The recommendation of taxi pickup locations plays an important role for drivers in carrying passengers efficiently. In addition, the emergence of the Internet of Vehicles provides technical support for it. However, existing recommendation methods do not model dynamic global positioning system information well and in real-time. In this article, we propose a spatio-temporal digraph convolutional network (STDCN) model. First, the pickup and drop-off locations are modeled into a directed spatio-temporal graph as input to the model. The correlation between each node is calculated as a unified edge weight based on the gray relational analysis. Then, the STDCN is used for dynamic spatio-temporal feature extraction. Finally, the edge-cloud collaboration framework is adopted to recommend local taxi pickup locations in real-time. The experimental results show that the proposed method is better than competing methods in terms of effectiveness and efficiency, and it shows good industrial conversion application prospects.

Predicting traffic accidents can help traffic management departments respond to sudden traffic situations promptly, improve drivers’ vigilance, and reduce losses caused by traffic accidents. However, the causality of traffic accidents is complex and difficult to analyze. Most existing traffic accident prediction methods do not consider the dynamic spatio-temporal correlation of traffic data, which leads to unsatisfactory prediction accuracy. To address this issue, we propose a multi-task learning framework (TAP) based on the Spatio-temporal Variational Graph Auto-Encoders (ST-VGAE) for traffic accident profiling. We firstly capture the dynamic spatio-temporal correlation of traffic conditions through a spatio-temporal graph convolutional encoder and embed it as a low-latitude vector. Then, we use a multi-task learning scheme to combine external factors to generate the traffic accident profiling. Furthermore, we propose a traffic accident profiling application framework based on edge computing. This method increases the speed of calculation by offloading the calculation of traffic accident profiling to edge nodes. Finally, the experimental results on real datasets demonstrate that TAP outperforms other state-of-the-art baselines.

Graph Convolutional Networks (GCNs) have achieved impressive performance in a wide variety of areas, attracting considerable attention. The core step of GCNs is the information-passing framework that considers all information from neighbors to the central vertex to be equally important. Such equal importance, however, is inadequate for scale-free networks, where hub vertices propagate more dominant information due to vertex imbalance. In this paper, we propose a novel centrality-based framework named CenGCN to address the inequality of information. This framework first quantifies the similarity between hub vertices and their neighbors by label propagation with hub vertices. Based on this similarity and centrality indices, the framework transforms the graph by increasing or decreasing the weights of edges connecting hub vertices and adding self-connections to vertices. In each non-output layer of the GCN, this framework uses a hub attention mechanism to assign new weights to connected non-hub vertices based on their common information with hub vertices. We present two variants CenGCN_D and CenGCN_E, based on degree centrality and eigenvector centrality, respectively. We also conduct comprehensive experiments, including vertex classification, link prediction, vertex clustering, and network visualization. The results demonstrate that the two variants significantly outperform state-of-the-art baselines.

In this era of the Internet of Everything (IoE), edge computing has emerged as the critical enabling technology to solve a series of issues caused by an increasing amount of interconnected devices and large-scale data transmission. However, the deficiencies of edge computing paradigm are gradually being magnified in the context of IoE, especially in terms of service migration, security and privacy preservation, and deployment issues of edge node. These issues can not be well addressed by conventional approaches. Thanks to the rapid development of upcoming technologies, such as artificial intelligence (AI), blockchain, and microservices, novel and more effective solutions have emerged and been applied to solve existing challenges. In addition, edge computing can be deeply integrated with technologies in other domains (e.g., AI, blockchain, 6G, and digital twin) through interdisciplinary intersection and practice, releasing the potential for mutual benefit. These promising integrations need to be further explored and researched. In addition, edge computing provides strong support in applications scenarios, such as remote working, new physical retail industries, and digital advertising, which has greatly changed the way we live, work, and study. In this article, we present an up-to-date survey of the edge computing research. In addition to introducing the definition, model, and characteristics of edge computing, we discuss a set of key issues in edge computing and novel solutions supported by emerging technologies in IoE era. Furthermore, we explore the potential and promising trends from the perspective of technology integration. Finally, new application scenarios and the final form of edge computing are discussed.

Intelligent vehicle applications, such as autonomous driving and collision avoidance, put forward a higher demand for precise positioning of vehicles. The current widely used global navigation satellite systems (GNSS) cannot meet the precision requirements of the submeter level. Due to the development of sensing techniques and vehicle-to-infrastructure (V2I) communications, some vehicles can interact with surrounding landmarks to achieve precise positioning. Existing work aims to realize the positioning correction of common vehicles by sharing the positioning data of sensor-rich vehicles. However, the privacy of trajectory data makes it difficult to collect and train data centrally. Moreover, uploading vehicle location data wastes network resources. To fill these gaps, this article proposes a vehicle cooperative positioning (CP) system based on federated learning (FedVCP), which makes full use of the potential of social Internet of Things (IoT) and collaborative edge computing (CEC) to provide high-precision positioning correction while ensuring user privacy. To the best of our knowledge, this article is the first attempt to solve the privacy of CP from a perspective of federated learning. In addition, we take the advantages of local cooperation through vehicle-to-vehicle (V2V) communications in data augmentation. For individual differences in vehicle positioning, we utilize transfer learning to eliminate the impact of such differences. Extensive experiments on real data demonstrate that our proposed model is superior to the baseline method in terms of effectiveness and convergence speed.

This article presents a rolling optimization algorithm (ROA) for minimizing total vehicle delay for real-time traffic signal control in urban road networks, where the vehicle delay minimization problem is formulated as a quadratically constrained quadratic programming problem, which is NP-hard. The programming problem is relaxed and resolved using the ROA in a distributed manner. In particular, we introduce network partition to a large-scale urban road network and then present a regional ROA to eliminate the low efficiency caused by the large number of decision variables. Numerical experiments are performed on one of Beijing’s district road networks to validate the efficiency of the proposed method in benchmark against several typical techniques.

Mobile network operators (MNOs) allocate computing and caching resources for mobile users by deploying a central control system. Existing studies mainly use programming and heuristic methods to solve the resource allocation problem, which ignores the energy cost problem that is really significant to the MNO. To solve this problem, in this article, we design a joint computing and caching framework by integrating deep deterministic policy gradient (DDPG) algorithm. Especially, we focus on the Internet of Vehicles scenario, which needs the support of mobile network provided by MNO. We first formulate an optimization problem to minimize MNO’s energy cost by considering the computation and caching energy costs jointly. Then, we turn the formulated problem into a reinforcement learning problem and utilize DDPG methods to solve this problem. The final simulation result shows that our solution can reduce energy costs by more than 15%, while ensuring the tasks can be completed on time.

Vehicle-to-vehicle interaction and collaboration can provide us with a large number of mobile traffic trajectories that can be used to analyze driving behavior. In this article, we propose a spatio-temporal cost combination based framework for taxi driving fraud detection. First, the point of interest where taxis interact and collaborate with collaborative Internet of Vehicles participants is identified, and a baseline trajectory model is built to determine the typical trajectory distribution. Second, a statistical model is used to calculate the travel distribution, travel time, and travel cost. At the same time, the taxi trajectory points are converted into evolving graphs to detect the abnormality of the local road segment. Then, we can analyze the causes of outlier trajectories combined with the perception of abnormal road environments. Finally, the trajectories of real taxis were used to evaluate outliers, which proves the effectiveness and efficiency of the method.

Modern enterprises attach much attention to the selection of commercial locations. With the rapid development of urban data and machine learning, we can discover the patterns of human mobility with these data and technology to guide commercial district discovery. In this article, we propose an unsupervised commercial district discovery framework via embedding space clustering on graph convolution networks to solve the problem of commercial district discovery. Specifically, the proposed framework aggregates human mobility features according to geographic similarity by graph convolution networks. Based on the graph convolution network embedding space, we apply hierarchical clustering to mine the latent functional regions hidden in different human patterns. Then, with the kernel density estimation, we can obtain the semantic labels for the clustering results to discover commercial districts. Finally, we analyze the multisource data of the Xiaoshan District and Chengdu City, and experiments verify the effectiveness of our framework.

Traffic flow prediction is an integral part of an intelligent transportation system and thus fundamental for various traffic-related applications. Buses are an indispensable way of moving for urban residents with fixed routes and schedules, which leads to latent travel regularity. However, human mobility patterns, specifically the complex relationships between bus passengers, are deeply hidden in this fixed mobility mode. Although many models exist to predict traffic flow, human mobility patterns have not been well explored in this regard. To address this research gap and learn human mobility knowledge from this fixed travel behaviors, we propose a multi-pattern passenger flow prediction framework, MPGCN, based on Graph Convolutional Network (GCN). Firstly, we construct a novel sharing-stop network to model relationships between passengers based on bus record data. Then, we employ GCN to extract features from the graph by learning useful topology information and introduce a deep clustering method to recognize mobility patterns hidden in bus passengers. Furthermore, to fully utilize spatio-temporal information, we propose GCN2Flow to predict passenger flow based on various mobility patterns. To the best of our knowledge, this paper is the first work to adopt a multi-pattern approach to predict the bus passenger flow by taking advantage of graph learning. We design a case study for optimizing routes. Extensive experiments upon a real-world bus dataset demonstrate that MPGCN has potential efficacy in passenger flow prediction and route optimization.

Congestion recognition is necessary for vehicle routing, traffic control, and many other applications in intelligent transportation systems. Besides, traffic facilities in the three-dimensional road network, which contains the fundamental spatiotemporal features for congestion recognition, provides multi-source traffic information. To exploit these traffic big data, in this paper, we propose an attention mechanism-based digraph convolution network (ADGCN) enabled framework to tackle the congestion recognition problem. It can be divided into two parts, spatial relevance modeling and temporal relevance modeling. At first, the representation incorporates spatiotemporal traffic information with the three-dimensional urban network, and partially decouples the global network topology to a single-knot digraph. Then a digraph-based convolution network is used to capture high-order spatial features. Finally, to proceed with time-series features, the multi-modal attention mechanism is introduced to catch the long-range temporal dependence and the congestion classifier is defined accordingly. This distinguishes the proposed model from the conventional congestion recognition methods. Comprehensive experiments are conducted based on real traffic data. The results demonstrate the advantages of the proposed framework over the existing spatiotemporal analysis methods.

Due to the incomplete coverage and failure of traffic data collectors during the collection, traffic data usually suffers from information missing. Achieving accurate imputation is critical to the operation of transportation networks. Existing approaches usually focus on the characteristic analysis of temporal variation and adjacent spatial representation, and the consideration of higher‐order spatial correlations and continuous data missing attracts more attentions from the academia and industry. In this paper, by leveraging motif‐based graph aggregation, we propose a spatiotemporal imputation approach to address the issue of traffic data missing. First, through motif discovery, the higher‐order graph aggregation model was presented in traffic networks. It utilized graph convolution network (GCN) to polymerize the correlated segment attributes of the missing data segments. Then, the multitime dimension imputation model based on bidirectional long short‐term memory (Bi‐LSTM) incorporated the recent, daily‐periodic, and weekly‐periodic dependencies of the historical data. Finally, the spatial aggregated values and the temporal fusion values were integrated to obtain the results. We conducted comprehensive experiments based on the real‐world dataset and discussed the case of random and continuous data missing by different time intervals, and the results showed that the proposed approach was feasible and accurate.

Embedded systems are common in the Internet of Things domain: their integration in vehicles and mobile devices is being fostered in the Internet of vehicles (IoV). IoV has direct applications on intelligent transportation systems and smart cities. Besides basic requirements, such as ease of installation, cost-effectiveness, scalability, and flexibility, IOV applications need to guarantee energy-efficient and good-quality communication. In fact, IoV implementations are commonly based on wireless nodes, which rely on a limited energy source; therefore, an efficient communication among the nodes is desirable to prolong the lifetime of the devices. In particular, the alternation of active and sleep states and the regulation of the transmission power represent two common approaches to save energy. Based on this strategy, an effective fuzzy control system is presented in the paper to manage power consumption and quality of services of IoV applications. Two fuzzy controllers increase the battery life while keeping a good throughput to workload ratio. This technique has been simulated with two leading technologies in IoV: IEEE 802.11b/g/n and IEEE 802.11p. Experimental results show a network lifetime improvement ranging from 30% to 40%, according to the adopted medium access control protocol.

During the outbreak of the Coronavirus disease 2019 (COVID-19), while bringing various serious threats to the world, it reminds us that we need to take precautions to control the transmission of the virus. The rise of the Internet of Medical Things (IoMT) has made related data collection and processing, including healthcare monitoring systems, more convenient on the one hand, and requirements of public health prevention are also changing and more challengeable on the other hand. One of the most effective nonpharmaceutical medical intervention measures is mask wearing. Therefore, there is an urgent need for an automatic real-time mask detection method to help prevent the public epidemic. In this article, we put forward an edge computing-based mask (ECMask) identification framework to help public health precautions, which can ensure real-time performance on the low-power camera devices of buses. Our ECMask consists of three main stages: 1) video restoration; 2) face detection; and 3) mask identification. The related models are trained and evaluated on our bus drive monitoring data set and public data set. We construct extensive experiments to validate the good performance based on real video data, in consideration of detection accuracy and execution time efficiency of the whole video analysis, which have valuable application in COVID-19 prevention.

License plate is an essential characteristic to identify vehicles for the traffic management, and thus, license plate recognition is important for Internet of Vehicles. Since 5G has been widely covered, mobile devices are utilized to assist the traffic management, which is a significant part of Industry 4.0. However, there have always been privacy risks due to centralized training of models. Also, the trained model cannot be directly deployed on the mobile device due to its large number of parameters. In this article, we propose a federated learning-based license plate recognition framework (FedLPR) to solve these problems. We design detection and recognition model to apply in the mobile device. In terms of user privacy, data in individuals is harnessed on their mobile devices instead of the server to train models based on federated learning. Extensive experiments demonstrate that FedLPR has high accuracy and acceptable communication cost while preserving user privacy.

As a new computing paradigm, edge computing emerges in various fields. Many tasks previously relied on cloud computing are distributed to various edge devices that cooperate to complete the tasks. However, circumstantial factors in the edge network (e.g., functionality, transmission efficiency, and resource limitation) become more complex than those in cloud computing. Consequently, there is instability that cannot be ignored in the cooperation between the edge devices. In this article, we propose a novel framework to optimize edge cooperative network (ECN), called ECN-Opt, to improve the performance of edge computing tasks. Specifically, we first define the evaluation metrics for cooperation. Next, the cooperation of an ECN is optimized to improve the performance of specific tasks. Extensive experiments using real datasets from wearable sensors on the players in soccer teams demonstrate that our ECN-Opt framework performs well, and it also validate the effectiveness of the proposed optimization algorithm.

With the widely used Internet of Things, 5G, and smart city technologies, we are able to acquire a variety of vehicle trajectory data. These trajectory data are of great significance which can be used to extract relevant information in order to, for instance, calculate the optimal path from one position to another, detect abnormal behavior, monitor the traffic flow in a city, and predict the next position of an object. One of the key technology is to cluster vehicle trajectory. However, existing methods mainly rely on manually designed metrics which may lead to biased results. Meanwhile, the large scale of vehicle trajectory data has become a challenge because calculating these manually designed metrics will cost more time and space. To address these challenges, we propose to employ network representation learning to achieve accurate vehicle trajectory clustering. Specifically, we first construct the k-nearest neighbor-based internet of vehicles in a dynamic manner. Then we learn the low-dimensional representations of vehicles by performing dynamic network representation learning on the constructed network. Finally, using the learned vehicle vectors, vehicle trajectories are clustered with machine learning methods. Experimental results on the real-word dataset show that our method achieves the best performance compared against baseline methods.

The advisor-advisee relationship represents direct knowledge heritage, and such relationship may not be readily available from academic libraries and search engines. This work aims to discover advisor-advisee relationships hidden behind scientific collaboration networks. For this purpose, we propose a novel model based on Network Representation Learning (NRL), namely Shifu2, which takes the collaboration network as input and the identified advisor-advisee relationship as output. In contrast to existing NRL models, Shifu2 considers not only the network structure but also the semantic information of nodes and edges. Shifu2 encodes nodes and edges into low-dimensional vectors respectively, both of which are then utilized to identify advisor-advisee relationships. Experimental results illustrate improved stability and effectiveness of the proposed model over state-of-the-art methods. In addition, we generate a large-scale academic genealogy dataset by taking advantage of Shifu2.

This paper addresses the issues of selfishness, limited network resources, and their adverse effects on real-time dissemination of Emergency Warning Messages (EWMs) in modern Autonomous Moving Platforms (AMPs) such as Vehicular Social Networks (VSNs). For this purpose, we propose a social intelligence based identification mechanism to differentiate between a selfish and a cooperative node in the network. Therefore, we devise a crowdsensing based mechanism to calculate a tie-strength value based on several social metrics. Moreover, we design a recursive evolutionary algorithm for each node’s reputation calculation and update. Given that, then we estimate each node’s state-transition probability to select a super-spreader for rapid dissemination. In order to ensure a seamless and reliable dissemination process, we incorporate 5G network structure instead of conventional short range communication which is used in most vehicular networks at present. Finally, we design a real-time dissemination algorithm for EWMs and evaluate its performance in terms of network parameters such as delivery-ratio, delay, hop-count, and message-overhead for varying values of vehicular density, speed, and selfish nodes’ density based on realistic vehicular mobility traces. In addition, we present a comparative analysis of the performance of the proposed scheme with state-of-the-art dissemination schemes in VSNs.

Mobile crowdsourcing (MCS), as an effective and crucial technique of Industrial Internet of Things, is enabling smart city initiatives in the real world. It aims at incorporating the intelligence of dynamic crowds to collect and compute decentralized ubiquitous sensing data that can be used to solve major urbanization problems such as traffic congestion. The shared bus, as a neotype transportation mode, aims at improving the resource utilization rate and maintaining the advantages of convenience and economy. In this article, we provide a scheme to profile shared buses through heterogeneous mobile crowdsourced data (TRProfiling). First, we design an MCS-based shared bus data generation and collection solution to overcome the aforementioned data scarcity issue. Then, we propose a travel profiling to profile resident travel and design a method called multiconstraint evolution algorithm to optimize the routes. Experimental results demonstrate that TRProfiling has an excellent performance in satisfying passengers’ travel requirements.

Complex networks have become an active interdisciplinary field of research inspired by the empirical study of various networks. A subway network is a real-world example of complex networks in the transportation domain, which has attracted growing attention in network analysis recently. Analyzing human mobility patterns, specifically in ranking subway stations closely bounded by urban subway planning and individuals' travel experience, is still an open issue. In this paper, we propose a novel ranking method of station importance (SIRank) by utilizing human mobility patterns and improved PageRank algorithm. Specifically, by analyzing human mobility patterns of the subway system in Shanghai, we demonstrate both static and dynamic characteristics using two network models (Shanghai subway static network and Shanghai subway passenger network). In particular, the SIRank focuses on bi-directional passenger flow analysis between origins and destinations to iteratively generate the importance value for each station. We implement a range of the experiments to illustrate the effectiveness of SIRank using the real-world subway transaction datasets. The results demonstrate that the hit ratio in SIRank reaches 60% in the top five stations, which is much higher than that of ranking by a weighted mixed index (WMIRank) and ranking by node degree (NDRank) approaches.

This article elaborates how to identify and evaluate causal factors to improve scientific impact. Currently, analyzing scientific impact can be beneficial to various academic activities including funding application, mentor recommendation, discovering potential cooperators, and the like. It is universally acknowledged that high-impact scholars often have more opportunities to receive awards as an encouragement for their hard work. Therefore, scholars spend great efforts in making scientific achievements and improving scientific impact during their academic life. However, what are the determinate factors that control scholars’ academic success? The answer to this question can help scholars conduct their research more efficiently. Under this consideration, our article presents and analyzes the causal factors that are crucial for scholars’ academic success. We first propose five major factors including article-centered factors, author-centered factors, venue-centered factors, institution-centered factors, and temporal factors. Then, we apply recent advanced machine learning algorithms and jackknife method to assess the importance of each causal factor. Our empirical results show that author-centered and article-centered factors have the highest relevancy to scholars’ future success in the computer science area. Additionally, we discover an interesting phenomenon that the h -index of scholars within the same institution or university are actually very close to each other.

Aiming at the problem of intersection signal control, a method of traffic phase combination and signal timing optimization based on the improved K-medoids algorithm is proposed. Firstly, the improvement of the traditional K-medoids algorithm embodies in two aspects, namely, the selection of the initial medoids and the parameter k , which will be applied to the cluster analysis of historical saturation data. The algorithm determines the initial medoids based on a set of probabilities calculated from the distance and determines the number of clusters k based on an exponential function, weight adjustment, and elbow ideas. Secondly, a phase combination model is established based on the saturation and green split data, and the signal timing is optimized through a bilevel programming model. Finally, the algorithm is evaluated over a certain intersection in Hangzhou, and results show that this algorithm can reduce the average vehicle delay and queue length and improve the traffic capacity of the intersection in the peak hour.

The car-following model describes the microscopic behavior of the vehicle. However, the existing car-following models set the drivers’ reaction time to a fixed value without considering its dynamics. In order to improve the accuracy of car-following model, this paper proposes Deep Feature Learning-based Car-Following Model (DeepCF), a car-following model based on fatigue driving and Generative Adversarial Networks (GAN). The model is composed of the drivers’ reaction time model and the car-following decision algorithm. First, we regard driving fatigue as the starting point to study the influence of driving time and the acceleration of the preceding vehicle on the drivers’ reaction time, and develop a coarse-grained drivers’ reaction time model. Secondly, considering the impact of fatigue driving on car-following decisions, we utilize GAN to generate a driving decision database based on reaction time and use Euclidean distance as a decision search indicator. Finally, we conduct experiments on a real data set, and the results indicate that our DeepCF model is superior to baseline models.

In order to save time and cost, many disease gene prediction methods have been proposed in recent years. However, the traditional network model uses a binary relationship to represent the relationship between different proteins or gene molecules and phenotypes, which leads to the loss of information. Recently, hypergraph shows that it can overcome this loss of information to some extent and preserve the multivariate relationship, so we transformed the disease gene prediction problem into the problem of ranking the multivariate-relationship object. In this paper, we propose a method of Heterogeneous Probabilistic Hypergraph Ranking (HPHR) to predict disease genes. Firstly, fix a graph centroid for each hyperedge and according to different associations, and add other nodes related to the graph centroid to hyperedges with a certain probability. Then transform the problem of predicting disease genes into the problem of ranking heterogeneous objects, and the candidate genes are sorted by hypergraph ranking. The method is then applied to the integrated disease gene network. Compared with other prediction methods achieved better results, which was verified by this experiment.

Local administrations and governments aim at leveraging wireless communications and Internet of Things (IoT) technologies to manage the city infrastructures and enhance the public services in an efficient and sustainable manner. Furthermore, they strive to adopt smart and cost-effective mobile applications to deal with major urbanization problems, such as natural disasters, pollution, and traffic congestion. Mobile crowdsourcing (MCS) is known as a key emerging paradigm for enabling smart cities, which integrates the wisdom of dynamic crowds with mobile devices to provide decentralized ubiquitous services and applications. Using MCS solutions, residents (i.e., mobile carriers) play the role of active workers who generate a wealth of crowdsourced data to significantly promote the development of smart cities. In this paper, we present an overview of state-of-the-art technologies and applications of MCS in smart cities. First, we provide an overview of MCS in smart cities and highlight its major characteristics. Second, we introduce the general architecture of MCS and its enabling technologies. Third, we study novel applications of MCS in smart cities. Finally, we discuss several open problems and future research challenges in the context of MCS in smart cities.

With the evolutionary development of latency sensitive applications, delay restriction is becoming an obstacle to run sophisticated applications on mobile devices. Partial computation offloading is promising to enable these applications to execute on mobile user equipments with low latency. However, most of the existing researches focus on either cloud computing or mobile edge computing (MEC) to offload tasks. In this paper, we comprehensively consider both of them and it is an early effort to study the cooperation of cloud computing and MEC in Internet of Things. We start from the single user computation offloading problem, where the MEC resources are not constrained. It can be solved by the branch and bound algorithm. Later on, the multiuser computation offloading problem is formulated as a mixed integer linear programming problem by considering resource competition among mobile users, which is NP-hard. Due to the computation complexity of the formulated problem, we design an iterative heuristic MEC resource allocation algorithm to make the offloading decision dynamically. Simulation results demonstrate that our algorithm outperforms the existing schemes in terms of execution latency and offloading efficiency.

A co-occurrence pattern is an interesting pattern in human mobility, which has essential values in business intelligence, social activities, and urban planning. However, due to the deluge and complexity of mobile big data, as well as the complicated intrinsic features of the co-occurrence pattern, mining and analyzing the co-occurrence pattern are computationally highly expensive. Therefore, in this paper, we propose a framework to mine co-occurrence event data from mobile data and to explore the urban co-occurrence pattern visually. Our framework contains two modules: data modeling, to obtain the co-occurrence event data effectively utilizing frequent itemsets mining algorithm based on traffic GPS records, and visualization, to explore the co-occurrence pattern in urban scenarios from global, regional, statistical, and location perspectives. Our visualization system has been demonstrated using case studies with a real-world data set.

The year of 2017 for the 50th anniversary of the Turing Award, which represents the top-level award in the computer science field, is a milestone. We study the long-term evolution of the Turing Award Collaboration Network, and it can be considered as a microcosm of the computer science field from 1974 to 2016. First, scholars tend to publish articles by themselves at the early stages, and they began to focus on tight collaboration since the late 1980s. Second, compared with the same scale random network, although the Turing Award Collaboration Network has small-world properties, it is not a scale-free network. The reason may be that the number of collaborators per scholar is limited. It is impossible for scholars to connect to others freely (preferential attachment) as the scale-free network. Third, to measure how far a scholar is from the Turing Award, we propose a metric called the Turing Number (TN) and find that the TN decreases gradually over time. Meanwhile, we discover the phenomenon that scholars prefer to gather into groups to do research with the development of computer science. This article presents a new way to explore the evolution of academic collaboration network in the field of computer science by building and analyzing the Turing Award Collaboration Network for decades.

Public transport is of great significance in megacities. Transit-oriented development (TOD) has become a reliable solution to urban sustainable development, which can reshape the urban form and improve its quality. This paper focuses on leveraging heterogeneous mega urban data to answer three critical questions in TOD: what region looks like under TOD concept, which regions have the potential to be TOD regions, and how to construct these TOD regions. For region partition, we propose a connected component-based clustering algorithm, which merges the large amount of public transport stops into representative cluster ones as region centers, and then apply the Voronoi algorithm to locate the region boundaries according to the cluster centers. For TOD region identification, we present a link importance-based random walk method that considers the importance of various transits and further identifies the most valuable regions to be TOD. For discovering functions of TOD regions, we introduce a multifactor-based function characterization approach that combines both the static linguistic factor and human mobility factor together and then derives the actual function distributions. The experiments, which are conducted on three real data sets, show the superiority of the proposed methods to solve the problems of region partition, TOD region identification, and function characterization for the megacities. In the meantime, the results provide support for the government to formulate public policy to construct a TOD city.

It is always difficult for researchers to find a new collaborator due to the academic information overload. Although several scientific collaborator recommendation systems have been proposed, most existing methods cannot meet scholars’ requirements comprehensively due to the neglect of sustainable collaborations in their schemes. Studies have shown that a sustainable collaboration has a significant positive impact on the productivity and influence for a researcher. To address this problem, we develop a sustainable collaborator recommendation (SCORE) system by utilizing the weak tie relationships brought by academic conferences for SCORE. Through defining and quantifying the conference closure, we incorporate the conference coattending relationships into collaborator recommendation system inspired by the principles of “diversified recommendation.” The experimental results on attendees of ten academic conferences show that the SCORE outperforms state-of-the-art collaborator recommendation systems in accuracy and sustainability. Our model can be used to improve the sociability of academic conferences by recommending sustainable collaborators to conference attendees.

Currently, vehicles have the abilities to communicate with each other autonomously. For Internet of Vehicles (IoV), it is urgent to reduce the latency and improve the throughput for data transmission among vehicles. This article proposes a deep learning based transmission strategy by exploring trirelationships among vehicles. Specifically, we consider both the social and physical attributes of vehicles at the edge of IoV, i.e., edge of vehicles. The social features of vehicles are extracted to establish the network model by constructing triangle motif structures to obtain primary neighbors with close relationships. Additionally, the connection probabilities of nodes based on the characteristics of vehicles and devices can be estimated, by which a content sharing partner discovery algorithm is proposed based on convolutional neural network. Finally, the experiment results demonstrate the efficiency of our method with respect to various aspects, such as message delivery ratio, average latency, and percentage of connected devices.

Aiming at the collision between the unmanned aerial vehicles (UAVs) in tracking motion target due to the inconsistency information, we design the unmanned aerial vehicle (UAV) formation cooperative tracking controller and analyze the flight-stability of the designed controller in the case of only knowing the UAV local information. Recently, a Lyapunov guidance vector field approach is proposed to achieve the desired circular trajectory in the paper. The path planning of a single UAV and the Multi-UAVs cooperative formation tracking motion target are simultaneously studied. In the ideal case, a guidance vector field method is proposed for the heading convergence, which has the advantage of analyzing and solving the collision problem between the unmanned aerial vehicles (UAVs). Further, a variable airspeed controller is used to maintain the UAV cooperative formation flight in a circular orbit, and an adaptive estimate is introduced to ensure the flight-stability of a circular orbit in the case of unknown wind and moving targets. In the process of the UAV formation tracking motion targets, we use a variable airspeed controller to achieve the desired angular spacing. In this paper, the designed controller laws are decentralized based on the local information. Meanwhile, the simulation shows that the designed controller has a good the flight-stability in the process of tracking motion target.

In recent years, many schemes have been proposed to disseminate Emergency Warning Messages (EWMs) in VANETs. However, various problems such as broadcast storm, hidden terminal and connectivity issues still persist to reduce the efficacy of these systems. In this paper, we propose a novel Social Utility-based Dissemination Scheme (SUDS) for Emergency Warning Messages in Vehicular Social Networks (VSNs). We utilize social properties of nodes such as centrality, interests and friendships to mitigate broadcast storm and hidden terminal problems. Furthermore, we employ the hybrid architecture of VSNs to solve connectivity and contact duration-related issues in sparse and high mobility environments. For this purpose, we devise a dual-strategy-based mechanism, where vehicles communicate with each other in a distributed or centralized manner according to the required situation. In order to evaluate the performance of proposed scheme, we have conducted extensive experiments for a highway scenario under varying vehicular density, vehicular speed and distance, in comparison with the state-of-the-art dissemination schemes. Simulation results have demonstrated the superiority of SUDS over the compared protocols in terms of delivery ratio, transmission delay and total number of transmissions. We have also demonstrated the positive effects of hybrid architecture of VSNs on various network parameters.

Due to the heterogeneous and resource-constrained characters of Internet of Things (IoT), how to guarantee ubiquitous network connectivity is challenging. Although LTE cellular technology is the most promising solution to provide network connectivity in IoTs, information diffusion by cellular network not only occupies its saturating bandwidth, but also costs additional fees. Recently, NarrowBand-IoT (NB-IoT), introduced by 3GPP, is designed for low-power massive devices, which intends to refarm wireless spectrum and increase network coverage. For the sake of providing high link connectivity and capacity, we stimulate effective cooperations among user equipments (UEs), and propose a social-aware group formation framework to allocate resource blocks (RBs) effectively following an in-band NB-IoT solution. Specifically, we first introduce a social-aware multihop device-to-device (D2D) communication scheme to upload information toward the eNodeB within an LTE, so that a logical cooperative D2D topology can be established. Then, we formulate the D2D group formation as a scheduling optimization problem for RB allocation, which selects the feasible partition for the UEs by jointly considering relay method selection and spectrum reuse for NB-IoTs. Since the formulated optimization problem has a high computational complexity, we design a novel heuristic with a comprehensive consideration of power control and relay selection. Performance evaluations based on synthetic and real trace simulations manifest that the presented method can significantly increase link connectivity, link capacity, network throughput, and energy efficiency comparing with the existing solutions.

In the new era of integrated computing with intelligent devices and system, moving aerial targets can be tracked flexibly. The estimation performance of traditional matched filter-based methods would deteriorate dramatically for multiple targets tracking, since the weak target is masked by the strong target or the strong sidelobes. In order to solve the problems mentioned above, this paper aims at developing a joint range-Doppler-angle estimation solution for an intelligent tracking system with a commercial frequency modulation radio station (noncooperative illuminator of opportunity) and a uniform linear array. First, a gridless sparse method is proposed for simultaneous angle-range-Doppler estimation with atomic norm minimization. Based on the integrated computing, multiple workstations or servers of the data process center in the intelligent tracking system can cooperate with each other to accelerate the data process. Then a suboptimal method, which estimates three parameters in a sequential way, is proposed based on grid sparse method. The range-Doppler of each target is iteratively estimated by exploiting the joint sparsity in multiple surveillance antennas. A simple beamforming method is used to estimate the angles in turn by exploiting the angle information in the joint sparse coefficients. Simulation result and real test show that the proposed solution can effectively detect weak targets in an iterative manner.

The development requirements of shared buses are extremely urgent to alleviate urban traffic congestions by improving road resource utilization and to provide a neotype transportation mode with good user experiences. The key to shared bus implementation lies in accurately predicting travel requirements and planning dynamic routes. However, the sparseness and the high volatility of shared bus data bring a great resistance to accurate prediction of travel requirements. Based on the consideration of user experiences, optimization objectives of shared bus route planning are significantly different from traditional public transportation and shared bus route planning is far more challenging than online car-hailing services due to the relatively high number of passengers. In this paper, we put forward a two-stage approach (SubBus), which is composed of travel requirement prediction and dynamic routes planning, based on various crowdsourced shared bus data to generate dynamic routes for shared buses in the “last mile” scene. First, we analyze the resident travel behaviors to obtain five predictive features, such as flow, time, week, location, and bus, and utilize them to predict travel requirements accurately based on a machine learning model. Second, we design a dynamic programming algorithm to generate dynamic, optimal routes with fixed destinations for multiple operating buses utilizing prediction results based on operating characteristics of shared buses. Extensive experiments are performed on real crowdsourced shared subway shuttle bus data and demonstrate that SubBus outperforms other methods on dynamic route planning for the “last mile” scene.Note to Practitioners—This paper is inspired by the problem of shared subway shuttle bus dynamic route planning for the “last mile” scene, and it is also applicable to other scenes, including commuting scenes, urban transportation hub scenes, and destination scenes of the tourist market. Shared bus operation routes at such scenes are usually aimed at trips with fixed destinations. Existing approaches to planning routes are generally designed for traditional transportation, such as traditional buses and taxis. In this paper, we propose a novel two-stage dynamic route planning approach (SubBus) based on the operation characteristics of shared subway shuttle buses. We perform a resident travel behavior analysis to improve the accuracy of travel requirement prediction. After that, we combine the prediction results and station properties to gain shared bus optimal routes. We then display how to apply SubBus to optimize shared bus operation status based on crowdsourced shared subway shuttle bus data generated by Panda Bus Company. We keep a continuous collaboration with the company to optimize the approach details and experimental effects, which demonstrate that our approach can generate effective routes for shared subway shuttle buses to optimize operation status on the “last mile” issue.

Context-aware applications of vehicular social networks (VSNs) play a significant role to achieve the goal of green transportation by sharing driving experience to reduce gasoline consumption. One of the main challenges is to evaluate the performance of these applications, which relies on the underlying VSN mobility model. In this paper, we investigate big urban traffic data to characterize essential features of urban mobility and construct large-scale green urban mobility models. We exploit the road and traffic information to enhance the trip generation algorithm and traffic assignment technique based on the weighted segments of roads. Besides, we perform extensive observations and corrections on the OpenStreetMap imported to simulation of urban mobility to make it analogous to real-world road topology. The experimental results and validation process show that the generated mobility models reveal realistic behavior required for analysis of context-aware applications of VSNs for green transportation systems.

Finding rising star in social networks becomes a popular research topic in recent years. Rising star means he or she may be not so charming at the initial time but turns out to be an outstanding star over time. In academic network, rising star means the scholar who just starts his research career with not so many papers published. While in the future, the sum of citations and papers will increase and the scholar will be more outstanding. There are some works of scholarly assessment, however, few works are about finding rising stars. Most of the algorithms of finding rising star are based on random walk on a heterogeneous network constructed by bibliography. These methods need entire information of networks and fit for a long time. In this paper, we propose a method based on “Social Genes”, which are defined as the inside factors of scholars' activities characteristics. We use factor analysis to find the inside factors, calculate the weights by neural network, and make assessments via AHP method. The experiment results on APS dataset show our method is able to find more authors with high rank.

Recently, urban traffic management has encountered a paradoxical situation which is the empty carrying phenomenon for taxi drivers and the difficulty of taking a taxi for passengers. In this paper, through analyzing the quantitative relationship between passengers' getting on and off taxis, we propose a time-location-relationship (TLR) combined taxi service recommendation model to improve taxi drivers' profits, uncover the knowledge of human mobility patterns, and enhance passengers' travel experience. Moreover, the TLR model uses Gaussian process regression and statistical approaches to acquire passenger volume, mean trip distance, and average trip time in functional regions during every period on weekdays and weekends, and allows drivers to pick up more passengers within a short time frame. Finally, we compare our proposed model with the autoregressive integrated moving average model, the back-propagation neural network model, the support vector machine model, and the gradient boost decision tree model by using the real taxi GPS data in Beijing. The experimental results show that our optimizing taxi service recommendation can predict more accurately than others by considering the 3-D properties.

On the basis of distributed traffic control framework, fuzzy theory, and artificial neural networks technique, the road network traffic intelligent coordination control technique with bus priority was proposed. The whole road network was regarded as a large-scale system, and the subsystems were the intersections. Multiphase intelligent signal controller that controlled its own traffic and cooperated with its neighbors was installed at each intersection. By exchanging information collected from its social vehicle detectors and the bus detection and location devices, and cooperating with adjacent signal controllers, social vehicle coordination and bus priority in the whole road network were realized. Bus priority module, green observation module, and phase switch module comprised the hard core of the controller. In each module, the fuzzy rule base system was designed in detail. To improve the control system's robusticity, the fuzzy relations of the three modules were implemented by one neural network. The target of this proposed method was to maximize the possibility for vehicles to depart from the upstream intersection, and the traveling bus nearby the local intersection to pass the local intersection without stoppage while the utility efficiency of the green signal time was at a relatively high level. The actual application shows that the proposed method can decrease the average vehicle delay and average travel time effectively.