Urban land use, intrinsically linked to people’s daily activities, undergoes continuous evolution, presenting a complex interplay that remains partially understood. To bridge this gap, our study leverages fine-grained human mobility data to predict these changes, adopting a novel approach that conceptualizes “community-level” land use shifts as a regression problem and represents citywide changes through dynamic graphs. We harness recent advancements in graph neural networks (GNNs), which, despite their success in various applications, face challenges in directly predicting land use changes due to the temporal mismatch between the slow evolution of urban land and the immediacy of human mobility data. Our research stands out by introducing a temporal skeleton for dynamic GNNs to synchronize human activity graphs with urban land use changes, a dynamic heterogeneous GNN approach for integrating diverse human activity data to capture essential temporal dependencies, and a novel algorithm powered by causal inference to elucidate the primary factors influencing land use predictions at the community level, all of which contribute to a training process informed by the generated causal graph. Empirically validated on three real-world datasets, our model demonstrates a performance leap over state-of-the-art baselines, marking a pivotal step toward understanding and predicting the dynamics of urban land use.

Real-world time series data is inherently complex, noisy, and exhibits abrupt changes, posing various challenges in data modeling. Given the ubiquity and importance of time-series data, accurately forecasting change points, instead of the overall predictive performance, has become increasingly attractive as it assists in risk mitigation and loss prevention. In this task, we argue that the past and future interactions involving the target points determine the comprehensive structure contributing to abrupt changes. However, traditional left-to-right auto-regressive approaches only consider the historical sequence, resulting in a flawed learning process and limited performance. In this paper, we extend the teacher-student learning and propose a novel S elf-optimizing T eacher and A uto-matching S tudent framework (named ST-AS) to predict change points in time series data. Our framework models change point representations specific to the target points by integrating future knowledge while avoiding data leakage. Specifically, we design a Gumbel-enhanced filter for our self-optimizing teacher, which constructs selected and filtered sub-groups to derive discriminative representations using a positive-unlabeled learning strategy. Given this well-trained teacher, we propose an adaptive pattern matcher for our auto-matching student model, which learns missing information by automatically aligning relevant features. After that, a novel two-stage dual-guided learning process is then designed to mimic teacher’s decision-making behavior and enhance student’s excavate capability. Finally, we conduct extensive experiments on four real-world datasets to demonstrate that our proposed ST-AS exhibits significantly better prediction performance compared to existing state-of-the-art alternatives.

Crowd logistics, as an emerging delivery paradigm, provides a cost-efficient way of leveraging crowdsourcing couriers to help express enterprises to match the surging delivery demands that are hard to be addressed by regular couriers only during online mega sale days. However, it is a challenging problem how to recruit an appropriate number of crowdsourcing couriers and assign an appropriate number of parcels to them and regular couriers, as many practical issues need to be considered, such as the dynamic competitive crowdsourcing market, the turnover of crowdsourcing couriers, and unique workload patterns of regular couriers. We design a crowdsourcing-assisted express system called COME to coordinate crowdsourcing and regular couriers for minimizing the overall cost of labor payment and parcel backlog. In COME, we design an Opponent-Aware Reinforcement Learning model to learn the recruitment difficulty in a competitive crowdsourcing market to make an appropriate recruitment plan, and design a four-staged approach to make an appropriate parcel assignment plan, which can address not only the dynamic recruitment difficulty but also the dynamic number of couriers. We have implemented and deployed COME on a real-world crowdsourcing-assisted express system in China involving 1358 delivery stations over 145 cities, and extensively evaluated it with a four-year real-world dataset, demonstrating its great advantage over other alternative solutions and showing high feasibility and generality.

Federated learning, as a distributed machine learning framework, enables clients to conduct model training without transmitting their data to the server, which is used to solve the dilemma of data silos and data privacy. It can work well on clients having similar data characteristics and distribution. However, it has some limitations where the dataset of clients may be different in distribution, quantity, and concept in many application scenarios. Personalized federated learning is a new federated learning paradigm that aims to guarantee client personalized models’ effectiveness when collaborating with the cloud server. Intuitively, providing further facilitated collaborations for the clients with similar data characteristics and distribution can benefit personalized model building. However, due to the invisibility of client data, it is challenging to extract client characteristics and define collaborative relationships among them from a fine-grained view. Moreover, a reasonable collaborative training approach needs to be designed for a distributed server–client framework. In this article, we design a Hierarchical Attention-enhanced Meta-learning Network (HAM) to address this issue. The main advantage of HAM is that it utilizes the meta-learning approach of taking model parameters as features and learns to learn an extra model for each client to analyze similarities according to their local dataset automatically. According to its two-layers framework, HAM can reasonably achieve a tradeoff between clients’ personality and commonality and provides a hybrid model with useful information from all clients. Considering there are two networks (HAM and base network) that need to learn for each client during the federated training process, we then provide an alternative learning approach to train them in an end-to-end fashion. To further clarify the approach, we describe the personalized federated learning settings framework as FedHAM where the HAM network is distributed deployed in each client. Extensive experiments based on two datasets prove that our method outperforms state-of-the-art baselines under different evaluation metrics.

For a new city that is committed to promoting Electric Vehicles (EVs), it is significant to plan the public charging infrastructure where charging demands are high. However, it is difficult to predict charging demands before the actual deployment of EV chargers for lack of operational data, resulting in a deadlock. A direct idea is to leverage the urban transfer learning paradigm to learn the knowledge from a source city, then exploit it to predict charging demands, and meanwhile determine locations and amounts of slow/fast chargers for charging stations in the target city. However, the demand prediction and charger planning depend on each other, and it is required to re-train the prediction model to eliminate the negative transfer between cities for each varied charger plan, leading to the unacceptable time complexity. To this end, we design an effective solution of S imultaneous Demand P rediction A nd P lanning ( SPAP ): discriminative features are extracted from multi-source data, and fed into an Attention-based Spatial-Temporal City Domain Adaptation Network ( AST-CDAN ) for cross-city demand prediction; a novel Transfer Iterative Optimization ( TIO ) algorithm is designed for charger planning by iteratively utilizing AST-CDAN and a charger plan fine-tuning algorithm. Extensive experiments on real-world datasets collected from three cities in China validate the effectiveness and efficiency of SPAP . Specially, SPAP improves at most 72.5% revenue compared with the real-world charger deployment.

Heterogeneous information network (HIN) embedding aims to learn the low-dimensional representations of nodes while preserving structures and semantics in HINs. Although most existing methods consider heterogeneous relations and achieve promising performance, they usually employ one single model for all relations without distinction, which inevitably restricts the capability of HIN embedding. In this paper, we argue that heterogeneous relations have different structural characteristics, and propose a novel Relation structure-aware HIN Embedding model, called RHINE. By exploring four real-world networks with thorough analysis, we present two structure-related measures which consistently distinguish heterogeneous relations into two categories: Affiliation Relations (ARs) and Interaction Relations (IRs). To respect the distinctive structural characteristics of relations, in RHINE, we propose different models specifically tailored to handle ARs and IRs, which can better capture the structures in HINs. Finally, we combine and optimize these models in a unified manner. Furthermore, considering that nodes connected via heterogeneous relations may have multi-aspect semantics and each relation focuses on one aspect, we introduce relation-specific projection matrices to learn node and relation embeddings in separate spaces rather than a common space, which can better preserve the semantics in HINs, referring to a new model RHINE-M. Experiments on four real-world datasets demonstrate that our models significantly outperform the state-of-the-art methods in four tasks.

Metadata is widely used to facilitate user defined queries and high-level event recognition applications in traffic surveillance videos. Current metadata extraction approaches rely on some computer vision algorithms, which are not accurate enough in the real world traffic scenes, and do not deal with big surveillance data efficiently. In this paper, we design a novel metadata extraction and metadata correction system. Firstly, we define the structure of metadata to determine which attribute (e.g., vehicle enter time, license plate number, vehicle type) we need to extract. Based on this structure, we employ a three-phase method to extract metadata. Secondly, we propose a graph-based metadata correction approach for compensating the accuracy of metadata extraction method. It fuses the big metadata of whole camera network, automatically detects suspicious metadata and corrects them based on the metadata spatial-temporal relationship and the image similarity. As the centralized framework may not be able to cope with the huge amount of data generated by traffic surveillance system, our system is implemented in a distributed fashion using Hadoop and HBase. Finally, the experimental results on real world traffic surveillance videos demonstrate the efficiency of our system, and also demonstrate that the metadata quality is significantly improved after metadata correction.

Reliable and real-time people counting is crucial in many applications. Most previous works can only count moving people from a single camera, which cannot count still people or can fail badly when there is a crowd (i.e., heavy occlusion occurs). In this article, we build a system for robust and fast people counting under occlusion through multiple cameras. To improve the reliability of human detection from a single camera, we use a dimensionality reduction method on the multilevel edge and texture features to handle the large variations in human appearance and poses. To accelerate the detection speed, we propose a novel two-stage cascade-of-rejectors method. To handle the heavy occlusion in crowded scenes, we present a fusion method with error tolerance to combine human detection from multiple cameras. To improve the speed and accuracy of moving people counting, we combine our multiview fusion detection method with particle tracking to count the number of people moving in/out the camera view (“border control”). Extensive experiments and analyses show that our method outperforms state-of-the-art techniques in single- and multicamera datasets for both speed and reliability. We also design a deployed system for fast and reliable people (still or moving) counting by using multiple cameras.