When planning intra-urban control measures for epidemics with significant societal impact, it is essential to consider the spatiotemporal orderliness of interventions, thus mitigating the disruption to daily life. For instance, improving intervention consistency among highly interacted sub-regions and avoid frequent and significant changes of interventions over time can be effective. However, existing studies on optimizing epidemic control overlooked the need for spatiotemporal consistency and stability of the interventions, potentially impacting their practicality and public compliance. To fill this gap, this study systematically conceptualized and quantified spatiotemporal orderliness for intra-urban epidemic control. A deep-reinforcement-learning (DRL) framework integrating the spatiotemporal orderliness was proposed to optimize the interventions across sub-regions over time. Taking Shenzhen, China as a study area, we solve a joint control plan for 74 sub-regions based on a meta-population SEIR epidemic model with a real-world intra-urban mobility network. The results demonstrate that the proposed model can effectively generate tailored dynamic interventions for sub-regions, significantly improving spatiotemporal orderliness. Furthermore, the effectiveness and generalizability of proposed model are demonstrated under different urban structures and transmissibility of respiratory viruses. Overall, this study provides a DRL-based tool for planning intra-urban epidemic control measures with enhanced spatiotemporal orderliness, potentially aiding future epidemic preparedness.

Individual trajectory data play a pivotal role in various application fields, such as urban planning, traffic control, and epidemic simulation. Despite the diverse means for data collection in current times, the real-world trajectory data in practical application remains severely limited due to concerns over personal privacy. In this study, we designed a Spatiotemporal-knowledge enhanced multi-TAsk GEnerative adversarial network (GAN), named STAGE, to generate synthetic trajectories that statistically resemble the real data without recycling personal information. In STAGE, we designed a multi-task generator with three stages of spatio-temporal generation tasks, i.e. activity-sequence generation task, township-level trajectory generation task, and neighborhood-level trajectory generation task, with the last one as the main task while the other two as auxiliary tasks. Meanwhile, we designed a spatial consistency loss in the adversarial training process to assess the spatial consistency of generated trajectories at different spatial scales. Experiment results show that compared to the baselines, trajectories generated by our method have closer data distributions to the real ones. We argued that the designs of spatiotemporal-knowledge enhanced generation tasks and training loss benefit the spatiotemporal generation processes, which help reproduce the temporal patterns of human daily activities and spatial distribution of human movements.

Understanding and simulating individual human mobility within intra-urban areas are essential for urban planning, transportation, public health, and other related fields. Currently, Random Walk-based and Exploration and Preferential Return (EPR)-based models are the primary mechanistic approaches used to model individual human mobility. However, these models often rely on assumptions and parameters derived from studies conducted at larger spatial scales, such as the national level. This results in inaccuracies when applied to urban contexts because individual mobility at the intra-urban scale exhibits unique spatiotemporal characteristics. Through a systematic analysis of three consecutive trajectory datasets and three check-in trajectory datasets collected from six cities worldwide, we identified two key insights into intra-urban mobility: (1) individuals’ waiting time distributions follow a log-normal function with universal parameters, rather than the gamma, power-law, or exponential functions commonly used in current studies, and (2) individuals’ spatial choice behavior is better captured by the Universal Opportunity (UO) model with universal parameters, rather than the frequently used gravity models. Based on these findings, we proposed Urban-EPR, a universal model specifically designed to simulate intra-urban individual mobility. Additionally, we compared seven mainstream mechanistic models, demonstrating the superior performance of Urban-EPR and providing a benchmark for intra-urban mobility research.

Accurate epidemic modeling requires detailed transmission networks, which are often approximated using population flow, but representing these networks is typically complex and computationally demanding. Traditional simplification methods rely on static indicators, ignoring the dynamic nature of human mobility and disease transmission. To address this, we introduce a novel method that simplifies population flow networks using geographic adjacency constraints and a dynamic transmission model. This method dynamically compares transmission characteristics to ensure critical human dynamics and transmission features are retained. We tested our method on Shenzhen’s 1 km grid-level network, showing it outperforms conventional approaches and higher-level spatial units like sub-districts. Our findings indicate that while overall bias in epidemic simulations remains low as network simplification increases, incorporating spatial factors leads to an increase in bias but reduces uncertainty. Notably, simulation errors rise sharply as the simplification rate nears complete simplification. Additionally, diseases with higher transmission rates exhibit larger simulation biases during network simplification. Our method significantly improves the accuracy of epidemic simulations while reducing computational complexity. The results have important implications for public health strategies, emphasizing the need for models that balance detail and feasibility to improve epidemic preparedness and response amid evolving human dynamics.

Understanding human mobility patterns is essential for various applications, from urban planning to public safety. The individual trajectory such as mobile phone location data, while rich in spatio-temporal information, often lacks semantic detail, limiting its utility for in-depth mobility analysis. Existing methods can infer basic routine activity sequences from this data, lacking depth in understanding complex human behaviors and users’ characteristics. Additionally, they struggle with the dependency on hard-to-obtain auxiliary datasets like travel surveys. To address these limitations, this paper defines trajectory semantic inference through three key dimensions: user occupation category, activity sequence, and trajectory description, and proposes the Trajectory Semantic Inference with Large Language Models (TSI-LLM) framework to leverage LLMs infer trajectory semantics comprehensively and deeply. We adopt spatio-temporal attributes enhanced data formatting (STFormat) and design a context-inclusive prompt, enabling LLMs to more effectively interpret and infer the semantics of trajectory data. Experimental validation on real-world trajectory datasets demonstrates the efficacy of TSI-LLM in deciphering complex human mobility patterns. This study explores the potential of LLMs in enhancing the semantic analysis of trajectory data, paving the way for more sophisticated and accessible human mobility research.

Human mobility data play a crucial role in many fields such as infectious diseases, transportation, and public safety. Although the development of Information and Communication Technologies (ICTs) has made it easy to collect individual-level positioning records, raw individual trajectory data are still limited in availability and usability due to privacy issues. Developing models to generate synthetic trajectories that are statistically close to the real data is a promising solution. This study proposed a novel trajectory generation method called Act2Loc (Activity to Location), which combined machine learning and mechanistic models. First, an activity-sequence generation model was constructed based on machine learning models (i.e. K-medoids and Transformer) to generate individual activity sequences aligning with human activity patterns. Then, a spatial-location selection model was proposed based on mechanistic models (e.g. Universal Opportunity model) to explicitly determine the specific locations of the activities in each generated sequence. Experimental results showed that compared to baselines based on purely machine learning or mechanistic models, Act2Loc can better reproduce the spatio-temporal characteristics of the real data, with additional advantage of low data requirements for training, proving its potential for generating synthetic trajectories in practice. This research offers new insights on knowledge-guided GeoAI models for human mobility.

Deep neural networks are being increasingly used for short-term traffic flow prediction, which can be generally categorized as CNNs or GNNs. CNNs typically partition an underlying territory into grid-like spatial units, and employ standard convolutions to learn spatial dependence among the units. However, standard convolutions with fixed geometric structures cannot fully model the nonstationary characteristics of local traffic flows. To overcome the deficiency, we introduce deformable convolution that augments the spatial sampling locations with additional offsets, to enhance the modeling capability of spatial nonstationarity. We design a deep deformable convolutional residual network, namely DeFlow-Net, that can effectively model global spatial dependence, local spatial nonstationarity, and temporal periodicity of traffic flows. Furthermore, to better fit with convolutions, we suggest to first aggregate traffic flows according to pre-conceived regions or self-organized regions based on traffic flows, then dispose to sequentially organized raster images for network input. Extensive experiments on real-world traffic flows demonstrate that DeFlow-Net outperforms GNNs and existing CNNs using standard convolutions, and spatial partition by pre-conceived regions or self-organized regions further enhances the performance. We also demonstrate the advantage of DeFlow-Net in maintaining spatial autocorrelation, and reveal the impacts of partition shapes and scales on deep traffic flow prediction.

Dynamic human activity intensity information is of great importance in many location-based applications. However, two limitations remain in the prediction of human activity intensity. First, it is hard to learn the spatial interaction patterns across scales for predicting human activities. Second, social interaction can help model the activity intensity variation but is rarely considered in the existing literature. To mitigate these limitations, we proposed a novel dynamic activity intensity prediction method with deep learning on graphs using the interactions in both physical and social spaces. In this method, the physical interactions and social interactions between spatial units were integrated into a fused graph convolutional network to model multi-type spatial interaction patterns. The future activity intensity variation was predicted by combining the spatial interaction pattern and the temporal pattern of activity intensity series. The method was verified with a country-scale anonymized mobile phone dataset. The results demonstrated that our proposed deep learning method with combining graph convolutional networks and recurrent neural networks outperformed other baseline approaches. This method enables dynamic human activity intensity prediction from a more spatially and socially integrated perspective, which helps improve the performance of modeling human dynamics.