With the advancement of agricultural modernization, agricultural machinery is widely used for crop harvesting. Traditionally, agricultural machines must be refueled at gas stations regularly, affecting the harvesting efficiency. A mobile refueling service has emerged in recent years, in which refueling tankers can move to serve the refueling request. However, the current mobile refueling system is still in an on-demand mode, which may not achieve timely response. Therefore, in this paper, we propose a new mobile refueling mode, i.e., predictive mobile refueling. To tackle the challenge of sparse rewards in predictive mobile refueling, we develop a two-stage reinforcement learning-based scheduling strategy MobRef, which decouples the scheduling process into a central request dispatcher and a distributed tanker reposition scheduler, and further introduces a potential energy-based reward shaping function to facilitate the training of the reposition scheduler. Extensive experiments on two real-world datasets demonstrate the effectiveness of MobRef, which outperforms the best baseline by 12.71% on average. We also present a deployed system based on MobRef, which is used internally in China National Petroleum Corporation.

Human mobility prediction is a fundamental technique for many urban applications, e.g., location-based recommendation, traffic scheduling, and travel demand prediction. Over the past decades, many methods, e.g., Markov Model, RNN, Transformer, have been leveraged to tackle the problem. However, existing approaches mainly train a supervised model based on an offline training dataset, which overlooks the phenomenon that the mobility behaviors of humans vary across time, and the trained models may not achieve ideal performance when applied to the testing data. To tackle this challenge, in this paper, we propose AdaMove, an efficient Test-Time Adaptive (TTA) model for human mobility prediction. AdaMove has a Preference-aware Test-Time Adaptation module called PTTA, which can adjust the parameters of a trained model based on the input test trajectory such that the model can generalize to the test distribution. In addition, to address the issue of reduced inference efficiency caused by parameter adjustment during the testing phase, AdaMove is equipped with a Lightweight human Mobility prediction model called LightMob, which only requires the recent trajectory as input to accelerate the inference. It is enhanced by historical trajectory knowledge via contrastive learning during the training time, so it has competitive performance compared with existing models. Extensive experiments on three real-world human mobility datasets demonstrate that AdaMove outperforms the best baseline by 9.3% on average in accuracy, and accelerates the inference speed by 28.5% on average compared with the original TTA - based inference.

Inferring the actual road segment purely based on one positioning point, known as single-point map matching (SMM), is vital for many urban applications, e.g., ride-hailing and geo-tagging. However, it is challenging due to inherent positioning errors and extrinsic heterogeneous environments. Existing methods either overlook the heterogeneity of different regions, or do not exploit the commonality of different matching tasks. In this paper, we treat each region as an individual SMM task to tackle the heterogeneity, and propose Spatial Hierarchical Meta-Learning for SMM (SHSMM) to learn the shared knowledge across tasks. SHSMM is equipped with a Dual-view Map Matcher to perform the matching, which can perceive the knowledge of road segments globally. To learn the task-specific model parameters, SHSMM modulates initial parameters and scales the local update learning rate based on hierarchical geographical and semantic knowledge about spatial tasks. A local update learning rate scheduling strategy is further proposed to facilitate the meta-training. Extensive experiments as well as case studies based on two real-world datasets demonstrate the effectiveness of the proposed method.

Crime risk map plays a crucial role in urban planning and public security management. Traditionally, it is obtained solely from historical crime incidents or inferred from limited environmental factors, which are not sufficient to accurately model the occurrences of crimes over the geographical space well. Motivated by the impressive and realistic conditional generating power of diffusion models, in this paper, we propose a multimodal conditional diffusion method, namely, DiffCrime, to infer the crime risk map based on datasets in various domains, i.e., historical crime incidents, satellite imagery, and map imagery. It is equipped with a history-gated multimodal denoising network, i.e., HamNet, dedicated to the crime risk map inference. HamNet emphasizes the importance of historical crime data via a Gated-based History Fusion (GHF) module and adaptively controls multimodal conditions to be fused across different diffusion time steps via a Time step-Aware Modality Fusion (TAMF) module. Extensive experiments on two real-world datasets demonstrate the effectiveness of DiffCrime, which outperforms baselines by at least 43% and 31% in terms of RMSE, respectively.

Discovering causal relationships among observed variables is a new research focus in the area of data mining. Methods based on the additive noise model have been proved to be efficient in the identification of cause-effect pairs. However, when trying to determine many-to-one causality, additive noise models often fail to identify the causal direction due to the complex interrelationships and interactions even though the generation of each causal relation follows the additive noise model, and become unreliable in practical applications. In this work, to identify the causal direction, we propose a Hierarchical Additive Noise Model (HANM) to convert many-to-one causality into an approximate one-to-one causality by generalizing multiple factors into an intermediate variable with a variational approach, and use asymmetry in the forward model and backward model of HANM to identify causal direction. Experiments using synthetic data show that many-to-one causality can be effectively identified through asymmetry with our proposed HANM and the accuracy of HANM is higher than the best existing model. By applying the model to real-world data, it can be seen that HANM can greatly augment the application scope of functional causal models for causal discovery.

In this paper, we propose a Hierarchical Sampling-based Multi-Instance ensemble LEarning (HSMILE) method. Due to the unique multi-instance learning nature, a positive bag contains at least one positive instance whereas samples (instance and sample are interchangeable terms in this paper) in a negative bag are all negative, simply applying bootstrap sampling to individual bags may severely damage a positive bag because a sampled positive bag may not contain any positive sample at all. To solve the problem, we propose to calculate probable positive sample distributions in each positive bag and use the distributions to preserve at least one positive instance in a sampled bag. The hierarchical sampling involves inter- and intrabag sampling to adequately perturb bootstrap sample sets for multi-instance ensemble learning. Theoretical analysis and experiments confirm that HSMILE outperforms existing multi-instance ensemble learning methods.