Inferring social relationships from mobility data is crucial for many applications because it reflects real-world connections among people. However, large-scale trajectory datasets with ground-truth social ties are exceedingly scarce, making it difficult to train deep models for relationship inference. To address this gap, we propose a transferable social relationship inference framework that can be trained on one high-quality, labeled dataset and then generalized to new datasets, even from different cities. Our framework rests on the key insight that social bonds depend largely on the frequency of individual meetings and the popularity of those meeting locations, both of which can be inferred statistically from raw trajectory data, irrespective of the underlying geographic semantics. It comprises two main modules: 1) Universal Social Relationship Classifier (USRC): A model trained to infer social relationships from trajectory data, and 2) Spatial Embedding Transfer (SET): A location embedding alignment technique that adapts new datasets to the pre-trained USRC model. By aligning location embeddings, SET module enables the pre-trained USRC to interpret previously unseen datasets without extra supervision. Experiments on five public datasets demonstrate that our method achieves state-of-the-art performance in zero-shot social relationship inference, surpassing other unsupervised, and in some cases, even supervised, approaches. Additionally, the SET module significantly improves location embedding alignment, outperforming existing baseline methods. The source code and data are available at https://github.com/chuchen2017/SET.

Given the temporal GPS coordinates from a large set of human agents, how can we model their mobility behavior toward effective anomaly (e.g., bad-actor or malicious behavior) detection without any labeled data? Human mobility and trajectory modeling have been extensively studied, showcasing varying abilities to manage complex inputs and balance performance-efficiency trade-offs. In this work, we formulate anomaly detection in complex human behavior by modeling raw GPS data as a sequence of stay-point events, each characterized by spatio-temporal features, along with trips (i.e., commutes) between the stay-points. Our problem formulation allows us to leverage modern sequence models for unsupervised training and anomaly detection. Notably, we equip our proposed model USTAD (for Uncertainty-aware Spatio-Temporal Anomaly Detection) with aleatoric (i.e., data) uncertainty estimation to account for inherent stochasticity in certain individuals' behavior, as well as epistemic (i.e., model) uncertainty to handle data sparsity under a large variety of human behaviors. Together, aleatoric and epistemic uncertainties unlock a robust loss function as well as uncertainty-aware decision-making in anomaly scoring. Extensive experiments show that USTAD significantly outperforms baselines in industry-scale data. We open-source all code at https://github.com/wenhaomin/USTAD.

Generating realistic human mobility data is essential for various application domains, including transportation, urban planning, and epidemic control, as real data is often inaccessible to researchers due to high costs and privacy concerns. Existing deep generative models learn from real trajectories to generate synthetic ones. Despite the progress, most of them suffer from training stability issues and scale poorly with increasing data size. More importantly, they often lack control mechanisms to guide the generated trajectories under constraints such as enforcing specific visits. To address these limitations, we formally define the controlled trajectory generation problem for effectively handling multiple spatiotemporal constraints. We introduce Geo-Llama, a novel LLM finetuning framework that can enforce multiple explicit visit constraints while maintaining contextual coherence of the generated trajectories. In this approach, pre-trained LLMs are fine-tuned on trajectory data with a visit-wise permutation strategy where each visit corresponds to a specific time and location. This strategy enables the model to capture spatiotemporal patterns regardless of visit orders while maintaining flexible and in-context constraint integration through prompts during generation. Extensive experiments on real-world and synthetic datasets validate the effectiveness of Geo-Llama, demonstrating its versatility and robustness in handling a broad range of constraints to generate more realistic trajectories compared to existing methods.

Human mobility modeling from GPS-trajectories and synthetic trajectory generation are crucial for various applications, such as urban planning, disaster management and epidemiology. Both of these tasks often require filling gaps in a partially specified sequence of visits, - a new problem that we call "controlled" synthetic trajectory generation. Existing methods for next-location prediction or synthetic trajectory generation cannot solve this problem as they lack the mechanisms needed to constrain the generated sequences of visits. Moreover, existing approaches (1) frequently treat space and time as independent factors, an assumption that fails to hold true in real-world scenarios, and (2) suffer from challenges in accuracy of temporal prediction as they fail to deal with mixed distributions and the inter-relationships of different modes with latent variables (e.g., day-of-the-week). These limitations become even more pronounced when the task involves filling gaps within sequences instead of solely predicting the next visit.

Modeling human mobility under uncertain conditions and individual preferences remains a difficult and unsolved problem. Data-driven deep learning approaches require extensive trajectory data for training, while more traditional methods often assume deterministic conditions or simple minimum-cost paths. We propose an implicit neural representation (INR) to learn continuous, latent fields of stochastic traffic properties over space and time. We successfully impute speeds on a road network with hundreds of thousands of edges from only a few hundred vehicles, then illustrate the quality of these representations on a trajectory generation task. A near-shortest-path algorithm weighted by the INR’s predictions produces plausible real-world routing choices, showing potential for applications in route planning and anomaly detection.

Human mobility research has significantly benefited from recent advances in machine learning, as have numerous other industries. Aided by the ever-increasing availability of geospatial and mobility data, machine learning models have enabled large-scale systems for simulating city-wide macro and micro mobility behaviors, urban planning, transportation management, and disaster relief optimization. However, while many fields have invested significant effort in solving the model transferability and generalization problem, the inability of machine learning-based human mobility models to generalize to new locations has come to be implicitly accepted in most geospatial research. In this vision paper, we focus on this geospatial generalization problem, its root causes, and how it is restricting the applications of otherwise-promising research. Most importantly, we argue for several data- and modeling-driven innovations which could help remedy this problem, spanning mega-scale simulations, large foundation models, and multi-task, transfer, and meta-learning. We also spotlight a handful of promising ideas which have recently emerged from the community. We hope that these proposals take root and help develop more capable, flexible, and generalizable models in research and industry.

The recent proliferation of real-world human mobility datasets has catalyzed geospatial and transportation research in trajectory prediction, demand forecasting, travel time estimation, and anomaly detection. However, these datasets also enable, more broadly, a descriptive analysis of intricate systems of human mobility. We formally define patterns of life analysis as a natural, explainable extension of online unsupervised anomaly detection, where we not only monitor a data stream for anomalies but also explicitly extract normal patterns over time. To learn patterns of life, we adapt Grow When Required (GWR) episodic memory from research in computational biology and neurorobotics to a new domain of geospatial analysis. This biologically-inspired neural network, related to self-organizing maps (SOM), constructs a set of "memories" or prototype traffic patterns incrementally as it iterates over the GPS stream. It then compares each new observation to its prior experiences, inducing an online, unsupervised clustering and anomaly detection on the data. We mine patterns-of-interest from the Porto taxi dataset, including both major public holidays and newly-discovered transportation anomalies, such as festivals and concerts which, to our knowledge, have not been previously acknowledged or reported in prior work. We anticipate that the capability to incrementally learn normal and abnormal road transportation behavior will be useful in many domains, including smart cities, autonomous vehicles, and urban planning and management.

A need to understand and predict vehicles' behavior underlies both public and private goals in the transportation domain, including urban planning and management, ride-sharing services, and intelligent transportation systems. Individuals' preferences and intended destinations vary throughout the day, week, and year: for example, bars are most popular in the evenings, and beaches are most popular in the summer. Despite this principle, we note that recent studies on a popular benchmark dataset from Porto, Portugal have found, at best, only marginal improvements in predictive performance from incorporating temporal information. We propose an approach based on hypernetworks, a variant of meta-learning ("learning to learn") in which a neural network learns to change its own weights in response to an input. In our case, the weights responsible for destination prediction vary with the metadata, in particular the time, of the input trajectory. The time-conditioned weights notably improve the model's error relative to ablation studies and comparable prior work, and we confirm our hypothesis that knowledge of time should improve prediction of a vehicle's intended destination.

This work introduces DIVINIA, a feature extractor and novel training objective for content-based image retrieval. DIVINIA combines a semantic matching objective with a ranking objective to produce a feature extractor that is able to retrieve semantically relevant regions from a large search corpus. It further ranks them appropriately according to visual similarity. Furthermore, DIVINIA provides a mechanism for performing one-shot and even zero-shot object localization without the need to fine-tune the feature extraction model or re-index the corpus of search features. We demonstrate the capabilities of the DIVINIA system in the context of object localization in satellite imagery. We present quantitative and qualitative results that show robust domain transfer between satellite image optics and sensor modalities. We show good precision and search relevance ordering when returning areas of interest to specific object classes.

It is common today for even consumer-grade cameras to tag images and videos with the location of the imagery on the earth's surface. Some imagery, however, does not have a geo-location tag and it thus becomes necessary to ascertain the location of the camera, image, or objects in the scene. For such imagery, users must work hard to deduce geo-locations using reference data. Geo-tagging of such image/video is an extremely time-consuming and labor-intensive activity that often meets with limited success. In this paper, we propose a system to estimate the geo-location and viewing direction of a query photo using geometric configuration of objects in the query. Our experiment using a set of ground-truth data within our proposed system shows promising results.