Ubiquitous sensors and mobile devices have spurred the growth of Web-of-Things (WoT) services in smart cities, making accurate spatio-temporal traffic predictions increasingly crucial. Leveraging advances in deep learning, recent Spatio-Temporal Graph Neural Networks (STGNNs) have achieved remarkable results. However, these methods address scenario-specific spatio-temporal heterogeneity by designing model architectures, often overlooking the importance of selecting optimal spatio-temporal knowledge (i.e., model inputs). In this paper, we propose an automated framework for spatio-temporal knowledge optimization to address this challenge. Our framework seamlessly integrates with downstream models, enhancing their performance across various prediction tasks. Specifically, we design a knowledge search space composed of parameters that represent scenario-specific spatio-temporal correlations within data. Additionally, we employ a bandit-based multi-fidelity algorithm for knowledge optimization to solve the constraint of limited resource. Furthermore, we adopt a meta-learner to extract transferable meta-knowledge about optimal knowledge, facilitating efficient exploration of the search space. Extensive experiments on five widely used real-world datasets demonstrate the effectiveness of our proposed framework. To the best of our knowledge, we are the first to automatically optimize spatio-temporal knowledge for spatio-temporal traffic prediction.

Contextual features are important data sources for building citywide crowd mobility prediction models. However, the difficulty of applying context lies in the unknown generalizability of contextual features (e.g., weather, holiday, and points of interests) and context modeling techniques across different scenarios. In this paper, we present a unified analytic framework and a large-scale benchmark for evaluating context generalizability. The benchmark includes crowd mobility data, contextual data, and advanced prediction models. We conduct comprehensive experiments in several crowd mobility prediction tasks such as bike flow, metro passenger flow, and electric vehicle charging demand. Our results reveal several important observations: (1) Using more contextual features may not always result in better prediction with existing context modeling techniques; in particular, the combination of holiday and temporal position can provide more generalizable beneficial information than other contextual feature combinations. (2) In context modeling techniques, using a gated unit to incorporate raw contextual features into the deep prediction model has good generalizability. Besides, we offer several suggestions about incorporating contextual factors for building crowd mobility prediction applications. From our findings, we call for future research efforts devoted to developing new context modeling solutions.

Spatiotemporal prediction (STP) service is one of the key infrastructure applications in smart cities. Currently, most of the existing STP services are constructed following the workflow of building deep learning (DL) applications while neglecting the importance of domain knowledge and region partition. However, the performance and interpretability of STP are highly related to them. As a result, there is an urgent requirement to develop a thorough and tailored workflow for STP services. To address this gap, we propose a novel workflow including two factors above as intermediate procedures. Based on the workflow, we design and implement an STP toolbox called UCTB (Urban Computing Tool Box) assisting practitioners in the rapid construction of STP services, which can manage multiple spatiotemporal do-main knowledge, support various region partition algorithms, and possess state-of-the-art models simultaneously. The relevant code and supporting documents have been open-sourced at https://github.com/uctb/UCIB.

Temporal (ST) prediction is crucial for making informed decisions in urban location-based applications like ride-sharing. However, existing ST models often require region partition as a prerequisite, resulting in two main pitfalls. Firstly, location-based services necessitate ad-hoc regions for various purposes, requiring multiple ST models with varying scales and zones, which can be costly to support. Secondly, different ST models may produce conflicting outputs, resulting in confusing predictions. In this paper, we propose One4All-ST, a framework that can conduct ST prediction for arbitrary modifiable areal units using only one model. To reduce the cost of getting multi-scale predictions, we design an ST network with hierarchical spatial modeling and scale normalization modules to efficiently and equally learn multi-scale representations. To address prediction inconsistencies across scales, we propose a dynamic programming scheme to solve the formulated optimal combination problem, minimizing predicted error through theoretical analysis. Besides, we suggest using an extended quad-tree to index the optimal combinations for quick response to arbitrary modifiable areal units in practical online scenarios. Extensive experiments on two real-world datasets verify the efficiency and effectiveness of One4All-ST in ST prediction for arbitrary modifiable areal units. The source codes and data of this work are available at https://github.com/uctb/One4All-ST.

Learning representations of user behavior sequences is crucial for various online services, such as online fraudulent transaction detection mechanisms. Graph Neural Networks (GNNs) have been extensively applied to model sequence relationships, and extract information from similar sequences. While user behavior sequence data volume is usually huge for online applications, directly applying GNN models may lead to substantial computational overhead during both the training and inference stages and make it challenging to meet real-time requirements for online services. In this paper, we leverage graph compression techniques to alleviate the efficiency issue. Specifically, we propose a novel unified framework called ECSeq, to introduce graph compression techniques into relation modeling for user sequence representation learning. The key module of ECSeq is sequence relation modeling, which explores relationships among sequences to enhance sequence representation learning, and employs graph compression algorithms to achieve high efficiency and scalability. ECSeq also exhibits plug-and-play characteristics, seamlessly augmenting pre-trained sequence representation models without modifications. Empirical experiments on both sequence classification and regression tasks demonstrate the effectiveness of ECSeq. Specifically, with an additional training time of tens of seconds in total on 100,000+ sequences and inference time preserved within 10−4seconds/sample, ECSeq improves the prediction [email protected] the widely used LSTM by ∼5%.

Spatio-temporal traffic prediction (STTP) plays a crucial role in the development of smart cities. Deep learning models have shown superior performance in traffic prediction, but their opacity and complexity of traffic data present challenges for researchers in tuning models. To tune models effectively, we propose a generalized error analysis pipeline and design a corresponding visualization system, STError-Copilot (Spatio-temporal Error Copilot). The pipeline analyzes multi-perspective spatio-temporal features to determine whether prediction errors originate from semantic or modeling levels, and subsequently tunes the model. STErrorCopilot provides a comprehensive data analysis solution, covering the entire workflow from data loading, processing and visualization to final tuning, delivering end-to-end services. We perform error analysis and tuning on two classic models using two real datasets, demonstrating that our method accurately identifies errors and provides appropriate tuning recommendations.

Urban events, such as hurricanes, floods, and epidemic outbreaks, usually have a significant impact on crowd behaviors that may dramatically change people’s mobility patterns, interaction manners, and etc. Accurately foreseeing the abnormal crowd behaviors triggered by urban events can help authorities to dynamically schedule urban resources and services, such as providing shuttle buses, temporal shelters, and psychological first aid. However, traditional crowd behavior prediction models tend to learn regular trends and patterns of crowd traffic, and usually fail to predict the abnormal traffic fluctuations triggered by urban events. In this work, we propose a context-aware framework to accurately predict event-triggered abnormal crowd traffic by explicitly modeling event contexts and their impact. We start by training a state-of-the-art spatiotemporal multi-graph model (MG) leveraging a multi-graph convolutional network (MGCN) to model city-wide venues and their connections, as well as gated recurrent unit (GRU) to capture the crowd traffic patterns within venues. In order to model the time-occasional impact of sudden influential event context (e.g., weather conditions), we propose a multi-task fusion diagram to co-train the MG model by learning a temporal impact embedding with an attention mechanism and recurrent neural network to obtain the MG-MT model. In order to model the hyper-space impact of irregularly distributed event context (e.g., epicenters and trajectories), we propose a multi-view fusion diagram to fine-tune the above-mentioned model by learning a spatial impact graph with a stimulus-response mechanism to obtain the spatiotemporal crowd traffic model (STORM). Experiments using real-world urban data collected from Xiamen, China show that our approach improves over the state-of-the-art baseline by more than 6% in predicting crowd traffic and by more than 40% in the abnormal crowd traffic part.

Most state-of-the-art deep domain adaptation techniques align source and target samples in a global fashion. That is, after alignment, each source sample is expected to become similar to any target sample. However, global alignment may not always be optimal or necessary in practice. For example, consider cross-domain fraud detection, where there are two types of transactions: credit and non-credit. Aligning credit and non-credit transactions separately may yield better performance than global alignment, as credit transactions are unlikely to exhibit patterns similar to non-credit transactions. To enable such fine-grained domain adaption, we propose a novel Knowledge-Inspired Subdomain Adaptation (KISA) framework. In particular, (1) We provide the theoretical insight that KISA minimizes the shared expected loss which is the premise for the success of domain adaptation methods. (2) We propose the knowledge-inspired subdomain division problem that plays a crucial role in fine-grained domain adaption. (3) We design a knowledge fusion network to exploit diverse domain knowledge. Extensive experiments demonstrate that KISA achieves remarkable results on fraud detection and traffic demand prediction tasks.

MAUP (modifiable areal unit problem) is a fundamental problem for spatial data management and analysis. As an instantiation of MAUP in online transportation platforms, region generation (i.e., specifying the areal unit for service operations) is the first and vital step for supporting spatiotemporal transportation services such as ride-sharing and freight transport. Most existing region generation methods are manually specified (e.g., fixed-size grids), suffering from poor spatial semantic meaning and inflexibility to meet service operation requirements. In this paper, we propose RegionGen, a data-driven region generation framework that can specify regions with key characteristics (e.g., good spatial semantic meaning and predictability) by modeling region generation as a multi-objective optimization problem. First, to obtain good spatial semantic meaning, RegionGen segments the whole city into atomic spatial elements based on road networks and obstacles (e.g., rivers). Then, it clusters the atomic spatial elements into regions by maximizing various operation characteristics, which is formulated as a multi-objective optimization problem. For this optimization problem, we propose a multi-objective co-optimization algorithm. Extensive experiments verify that RegionGen can generate more suitable regions than traditional methods for spatiotemporal service management.

The Spatio-Temporal Traffic Prediction (STTP) problem is a classical problem with plenty of prior research efforts that benefit from traditional statistical learning and recent deep learning approaches. While STTP can refer to many real-world problems, most existing studies focus on quite specific applications, such as the prediction of taxi demand, ridesharing order, traffic speed, and so on. This hinders the STTP research as the approaches designed for different applications are hardly comparable, and thus how an application-driven approach can be generalized to other scenarios is unclear. To fill in this gap, this paper makes three efforts: (i) we propose an analytic framework, called STAnalytic, to qualitatively investigate STTP approaches regarding their design considerations on various spatial and temporal factors, aiming to make different application-driven approaches comparable; (ii) we design a spatio-temporal meta-model, called STMeta, which can flexibly integrate generalizable temporal and spatial knowledge identified by STAnalytic, (iii) we build an STTP benchmark platform including ten real-life datasets with five scenarios to quantitatively measure the generalizability of STTP approaches. In particular, we implement STMeta with different deep learning techniques, and STMeta demonstrates better generalizability than state-of-the-art approaches by achieving lower prediction error on average across all the datasets.

Abstract The emphasis on research evaluation has brought scrutiny to the role of self‐citations in the scholarly communication process. While author self‐citations have been studied at length, little is known on national‐level self‐references (SRs). This paper analyses the citation context of national SRs, using the full‐text of 184,859 papers published in PLOS journals. It investigates the differences between national SRs and nonself‐references (NSRs) in terms of their in‐text mention, presence in enumerations, and location features. For all countries, national SRs exhibit a higher level of engagement than NSRs. NSRs are more often found in enumerative citances than SRs, which suggests that researchers pay more attention to domestic than foreign studies. There are more mentions of national research in the methods section, which provides evidence that methodologies developed in a nation are more likely to be used by other researchers from the same nation. Publications from the United States are cited at a higher rate in each of the sections, indicating that the country still maintains a dominant position in science. On the whole, this paper contributes to a better understanding of the role of national SRs in the scholarly communication system, and how it varies across countries and over time.

Reposting is the basic and key behavior for information diffusion in online social networks. It would be beneficial to understand the influence factors of reposting behavior and predict future reposting status, which could be practically applied in breaking news detection, marketing, social media researches and so on. Existing reposting analytics and prediction approaches mainly focus on factors related to the original information content and the social influence of the information publishers. However, online information diffuses by viral cascades instead of single-source broadcast in social network, which means some reposting behavior actually occurs in information propagators rather than the original publishers. In some social networks, users are allowed to comment when they repost, which represents their views and attitudes to the information they propagate. In this paper, we evaluate how emotional tendencies of information propagators influence future reposting. We first propose a modified sentiment analysis method and present emotional analysis on the user-generated content in online diffusion. Experiments are conducted with a real-world dataset and the results indicate the effectiveness of our fine-grained emotional features in reposting prediction.