Abnormal human mobility patterns often signal disruptions, emergencies, or health-related risks, making their detection critical for applications in public safety, urban monitoring, and healthcare. Existing approaches for human mobility anomaly detection typically focus on either identifying visits to unusual places or overall deviations from individual- and population-level norms at the agent-level. However, these methods often (1) overlook fine-grained temporal anomalies, and (2) lack interpretability, as they do not reveal which specific spatiotemporal components of a visit contribute to its anomalous nature. To overcome these limitations, we present ICAD (Interpretable Component-wise Anomaly Detection), a self-supervised autoregressive model that detects both spatial and temporal anomalies by modeling deviations in an individual's visit-level mobility behavior. ICAD is trained on normal visit sequences using a next-visit prediction objective to learn the distribution of visits under regular conditions. At inference, it computes component-wise anomaly scores for each visit by measuring relative divergence from the learned distribution of normal behavior. Specifically, ICAD proposes a top-k deviation metric for discrete spatial anomalies and introduces a novel relative mode-based scoring function for detecting temporal anomalies in continuous time. Experiments on a large scale synthetic human mobility dataset show that ICAD outperforms prior methods in both visit-level and agent-level anomaly detection. For reproducability purposes, the source code is accessible at https://github.com/USC-InfoLab/ICAD.

Road traffic congestion is a persistent problem. Focusing resources on the causes of congestion is a potentially efficient strategy for reducing slowdowns. We present NEXICA, an trained binary classifier to discover which parts of the highway system tend to cause slowdowns on other parts of the highway. We use time series of road speeds as inputs to our causal discovery algorithm, and we concentrate on just the presence or absence of events (traffic slowdowns). We test on six months of road speed data from 195 different highway speed sensors in the Los Angeles area, showing that our approach is superior to state-of-the-art baselines in both accuracy and computation speed.

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.

The abundance of vehicle trajectory data offers a new opportunity to compute driving routes between origins and destinations. Current graph-based routing pipelines, while effective, involve substantial costs in constructing, maintaining, and updating road network graphs to reflect real-time conditions. In this study, we propose a new trajectory-based routing paradigm that bypasses current workflows by directly utilizing raw trajectory data to compute efficient routes. Our method, named TrajRoute, uniquely “follows” historical trajectories from a source to a destination, constructing paths that reflect actual driver behavior and implicit preferences. To supplement areas with sparse trajectory data, the road network is also incorporated into TrajRoute's index, and tunable parameters are introduced to control the balance between road segments and trajectories, ensuring a unified and adaptable routing approach. We experimentally verify our approach by comparing it to an existing online routing service. Our results demonstrate that as the number of trajectories covering the road network increases, TrajRoute produces increasingly accurate travel time and route length estimates while gradually eliminating the need to downgrade to the road network. This highlights the potential of simpler, data-driven pipelines for routing, offering lowermaintenance alternatives to conventional systems.

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.

Human mobility data, represented as sequences of visits, are crucial for various application domains, including transportation, urban planning, and public health. However, large-scale human mobility data is typically inaccessible to researchers due to the high cost of data collection and privacy concerns. This limitation has led to several studies proposing learned models to generate synthetic visit sequences. Despite this progress, existing approaches lack mechanisms to control the generation process, which prevents the incorporation of prior knowledge and the spatiotemporal specification of certain visits. To address these limitations, we formally define the Constraint Trajectory Generation problem and introduce Geo-CETRA (Constraint Enforced Trajectory Generation), a novel framework that operates within the continuous spatiotemporal space, enabling direct generation of geographical coordinates and durations of each visit in a trajectory. Geo-CETRA reparameterizes the sampling space for effective enforcement of various spatiotemporal constraints. Furthermore, incorporating a constraint factorization approach along with an innovative beam decoding module, Geo-CETRA facilitates the production of high-quality synthetic trajectories that realistically emulate human movement while satisfying predefined spatiotemporal constraints. Experiments on real and synthetic datasets demonstrate Geo-CETRA's superior precision and contextual accuracy compared to existing approaches.

Understanding human mobility patterns is crucial for urban planning, resource allocation, and personalized recommendations. However, real-world trajectory data are rarely released publicly due to privacy concerns. At the same time, metropolitan cities are becoming equipped with various roadside sensors, such as CCTV cameras and RFIDs. Unlike trajectory data, these sensors do not uniquely identify and track vehicles, making extracting mobility patterns from their detections challenging. In this paper, we propose VPE, a framework that processes roadside sensor observations to estimate the probability that a vehicle visits a road segment at a certain time. At the core of VPE, we implement LEM, a novel mathematical model that calculates location transition probabilities taking into account the sensors’ reliability. Lastly, we propose APD+, an algorithm that captures the uncertainty of movement between two endpoints. Our experiments show that the proposed methods achieve high accuracy while maintaining practical computation time.

Location measurements from people are very often sparsely sampled due to power constraints or as an attempt at location privacy. However, we would still like to reason about location changes between samples in order to infer visits or understand moving behavior. In this work, we present a method for representing this location uncertainty while constraining the moving object to the road network, which is more realistic and precise for human mobility. Unlike the most straightforward method, fastest path, our method explicitly represents the location uncertainty between location measurements with probabilities. We introduce road network-based bridgelets, which are spatiotemporal probability clouds that model the location uncertainty between two endpoints, and we propose an algorithm, APD*, to generate bridgelets efficiently. In our experimental section, we evaluate the performance of APD* and provide visual examples to compare its output with other baseline methods.

Location measurements from people and vehicles often have long temporal gaps between them. However, we would still like to reason about location behavior during these gaps. This paper presents a new method for filling these gaps that is both principled and data-driven. Unlike the most common method, linear interpolation, our method explicitly represents the location uncertainty in the gaps with probability. It also learns from actual mobility data. We introduce bridgelets, which are small, spatio-temporal, maximum entropy clouds that model spatial uncertainty over small gaps. Using actual trajectories, we combine bridgelets into probabilistic bridges that are specific to absolute start and end locations on the map. The resulting bridges give the probability of visiting certain in-between locations given only the start and end points. Using real trajectory data, we compare our maximum entropy bridges to a popular baseline to show how our approach is much more accurate.

Regular people give away their location data without much knowledge about what can be inferred from it. This paper presents a sensitivity analysis of location disclosure, showing what can be inferred from even just a few place visits. The aim is to highlight how modern inference algorithms can take small location disclosures and create detailed inferences about where else a person is likely to go and what sorts of places they tend to visit. Based on an analysis of over 100,000 people, we show how disclosing just one location point can be used to predict other visits with an AUC of 0.88. We develop another algorithm that shows the types of places a person has an unusually high propensity to visit, and we use this result as part of an economic analysis of delivering targeted advertising. This research serves to raise awareness about what can be inferred from even very small location disclosures, which can in turn inform regular people about their true privacy risks.

Geofences are a fundamental tool of location-based services. A geofence is usually activated by detecting a location measurement inside the geofence region. However, location measurements such as GPS often appear sporadically on smart-phones, partly due to weak signals or privacy preservation. Users may restrict location sensing, or conserve energy, because sensing locations can consume a significant amount of battery. These unpredictable, and sometimes long, gaps between measurements mean that entry into a geofence can go completely undetected. In this paper we argue that short term location prediction can help alleviate this problem by computing the probability of entering a geofence in the future. Complicating this prediction approach is the fact that another location measurement could appear at any time, making the prediction redundant and wasteful. Therefore, we develop a framework that accounts for uncertain location predictions and the possibility of new measurements to trigger geofence activations. Our framework optimizes over the benefits and costs of correct and incorrect geofence activations, leading to an algorithm that reacts intelligently to the uncertainties of future movements and measurements.

The Brownian bridge is a method for probabilistically interpolating the location of a moving person, animal, or object between two measured points. This type of probabilistic interpolation is useful, because it represents the uncertainty of the interpolated points. It can be used to infer the probability of having visited a certain location, including possible exposure to disease. In the class of probabilistic interpolators, the Brownian bridge is attractive, because it has only a single adjustable parameter, the diffusion coefficient. This paper investigates the suitability of the Brownian bridge for interpolating human locations using mobility data from over 12 million people. One section looks at the consistency of the diffusion coefficient from person to person. As part of this, the paper presents, for the first time, a closed form solution for the maximum likelihood estimate of this parameter. The paper also presents statistical tests aimed at evaluating the accuracy of the Brownian bridge for interpolating human location.

A trajectory, defined as a sequence of location measurements, contains valuable information about movements of an individual. Its value of information (VOI) may change depending on the specific application. However, in a variety of applications, knowing the intrinsic VOI of a trajectory is important to guide other subsequent tasks or decisions. This work aims to find a principled framework to quantify the intrinsic VOI of trajectories from the owner's perspective. This is a challenging problem because an appropriate framework needs to take into account various characteristics of the trajectory, prior knowledge, and different types of trajectory degradation. We propose a framework based on information gain (IG) as a principled approach to solve this problem. Our IG framework transforms a trajectory with discrete-time measurements to a canonical representation, i.e., continuous in time with continuous mean and variance estimates, and then quantifies the reduction of uncertainty about the locations of the owner over a period of time as the VOI of the trajectory. Qualitative and extensive quantitative evaluation show that the IG framework is capable of effectively capturing important characteristics contributing to the VOI of trajectories.

Mobile apps that use location data are pervasive, spanning domains such as transportation, urban planning and healthcare. Important use cases for location data rely on statistical queries, e.g., identifying hotspots where users work and travel. Such queries can be answered efficiently by building histograms. However, precise histograms can expose sensitive details about individual users. Differential privacy (DP) is a mature and widely-adopted protection model, but most approaches for DP-compliant histograms work in a data-independent fashion, leading to poor accuracy. The few proposed data-dependent techniques attempt to adjust histogram partitions based on dataset characteristics, but they do not perform well due to the addition of noise required to achieve DP. We identify density homogeneity as a main factor driving the accuracy of DP-compliant histograms, and we build a data structure that splits the space such that data density is homogeneous within each resulting partition. We show through extensive experiments on large-scale real-world data that the proposed approach achieves superior accuracy compared to existing approaches.

Various phenomena such as viruses, gossips, and physical objects (e.g., packages and marketing pamphlets) can be spread through physical contacts. The spread depends on how people move, i.e., their mobility patterns. In practice, mobility patterns of an entire population is never available, and we usually have access to location data of a subset of individuals. In this paper, we formalize and study the problem of estimating the spread of a phenomena in a population, given that we only have access to sub-samples of location visits of some individuals in the population. We show that simple solutions that estimate the spread in the sub-sample and scale it to the population, or more sophisticated solutions that rely on modeling location visits of individuals do not perform well in practice. Instead, we directly model the co-locations between the individuals. We introduce PollSpreader and PollSusceptible, two novel approaches that model the co-locations between individuals using a contact network , and infer the properties of the contact network using the sub-sample to estimate the spread of the phenomena in the entire population. We analytically show that our estimates provide an upper bound and a lower bound on the spread of the disease in expectation. Finally, using a large high-resolution real-world mobility dataset, we experimentally show that our estimates are accurate in practice, while other methods that do not correctly account for co-locations between individuals result in entirely wrong observations (e.g, premature prediction of herd-immunity).

Location fingerprinting is a technique for determining the location of a device by measuring ambient signals such as radio signal strength, temperature, or any signal that varies with location. The accuracy of the technique is compromised by signal noise, quantization, and limited calibration resources. We develop generic, probabilistic models of location fingerprinting to find accuracy estimates. In one case, we look at predeployment modeling to predict accuracy before any signals have been measured using a new concept of noisy reverse geocoding. In another case, we model a previously deployed system to predict its accuracy. The models allow us to explore the accuracy implications of signal noise, calibration effort, and quantization of signals and space.

A geo-marketplace allows users to be paid for their location data. Users concerned about privacy may want to charge more for data that pinpoints their location accurately, but may charge less for data that is more vague. A buyer would prefer to minimize data costs, but may have to spend more to get the necessary level of accuracy. We call this interplay between privacy, utility, and price spatial privacy pricing. We formalize the issues mathematically with an example problem of a buyer deciding whether or not to open a restaurant by purchasing location data to determine if the potential number of customers is sufficient to open. The problem is expressed as a sequential decision making problem, where the buyer first makes a series of decisions about which data to buy and concludes with a decision about opening the restaurant or not. We present two algorithms to solve this problem, including experiments that show they perform better than baselines.

As any other type of data, GPS traces contain noise, anomaly, and sometimes unexpected values. Normally, researchers and data engineers analysts would start dealing with GPS data by removing those noises and outliers. However, in this work, we take the opposite direction. We focus on analyzing those unexpected values rather than discarding them. Interestingly, we discovered useful findings from an insight look at the noise in GPS trajectories. The intuition behind those discoveries is that when unexpected GPS readings are observed several times around a specific location, we study the nature of that location rather than thrown away those reading. By doing so, we are able to tell the type of area around those readings. For example, we can infer that a driver is passing by a tall building or through a forest based on the pattern of noise in the GPS readings. We are also able to question the quality of the underlying road map. Our findings and discoveries are based on the analysis of real GPS data for the Microsoft shuttles.

Human activity is one of the most important pieces of context affecting an individual's information needs. Understanding the relationship between activities, time, location, and other contextual features can improve the quality of various intelligent systems, including contextual search engines, task managers, digital personal assistants, chat bots, and recommender systems.

GPS data is noisy by nature. A typical location-based service would start by filtering out the noise from the raw GPS points that are generated by moving objects. Once the locations of the objects are identified, the location-based service is provided. In this paper, we decide not to throw away the noise. Instead, we consider the noise as an asset. We analyze the various noise patterns under different conditions and region characteristics. More specifically, we focus on one example where a lot of GPS noise is experienced; which is urban canyons. We believe that learning the GPS noise patterns in a supervised environment enables us to discover knowledge about new areas or areas where we have little knowledge. This paper is based on the analysis of GPS traces that are collected from the shuttle service within the Microsoft campuses around Seattle, Washington.

It is a fact that there are tons of GPS traces generated every minute by the millions of in-road vehicles over the world. Naturally, those traces contain imprecise readings, and most of the time they include noise and outliers. Therefore, there is a real need for a tool to allow users, companies, and researchers to get a deep insight into those raw traces and discover potential knowledge out of it. This knowledge would uncover the quality level of the GPS traces and, indeed, the quality level of the underlying map. It would also help discover interesting facts about the surrounding environment such as the type and height of buildings, the landscape cover, the weather conditions, and the nature of businesses and activities. This demo presents a system that allows users to interactively explore their collected GPS traces. Users can visually inspect the precision of their raw GPS traces, and snap these traces to the underlying road network map. Furthermore, users have the ability to clean their traces by applying various types of spatio-temporal filters. Users can perform noise analysis and produce statistics over regions of interest on the map. Last but not least, the system gives suggestions or guesses on the surrounding environment by comparing the perceived noise patterns to a database of pre-stored noise patterns. For the demo purpose, the system is initially populated with a rich data set of trajectories generated from the Microsoft shuttle service around the Greater Area of Seattle.

Location data from mobile devices is a sensitive yet valuable commodity for location-based services and advertising. We investigate the intrinsic value of location data in the context of strong privacy, where location information is only available from end users via purchase. We present an algorithm to compute the expected value of location data from a user, without access to the specific coordinates of the location data point. We use decision-theoretic techniques to provide a principled way for a potential buyer to make purchasing decisions about private user location data. We illustrate our approach in two scenarios: the delivery of targeted ads specific to a user's home location and the estimation of traffic speed. In both cases, the methodology leads to quantifiably better purchasing decisions than competing methods.

All current navigation systems return efficient source-to-destination routes assuming a "one-size-fits-all" set of objectives, without addressing most personal preferences. Although they allow some customization (like "avoid highways" or "avoid tolls"), the choices are very limited and require some sophistication on the part of the user. In this paper we present, implement, and test a framework that generates personalized driving directions by automatically analyzing users' GPS traces. Our approach learns cost functions using coordinate descent, leveraging a state-of-the-art route planning engine for efficiency. In an extensive experimental study, we show that this framework infers user-specific driving preferences, significantly improving the route quality. Our approach can handle continental-sized inputs (with tens of millions of vertices and arcs) and is efficient enough to be run on an autonomous device (such as a car navigation system) preserving user privacy.

We present a methodology for automatically extracting and summarizing reports of significant local events from large-scale Twitter feeds. While previous work has relied on an analysis of tweet text to identify local events, we show how to reliably detect events using only time series analysis of geotagged tweet volumes from localized regions. The algorithm sweeps through different spatial and temporal resolutions and finds events as anomalous spikes in the rate of geotagged tweets. We applied the approach to a corpus of over 733 million geotagged tweets. Using a panel of 103 crowdsourced judges who tagged 2400 detected events, we achieved a local event detection precision of 70%. Using these judged events as ground truth, a decision tree classifier was able to raise the detection precision to 93%.

The 20th ACM SIGSPATIAL Conference on Advances in Geographic Information Systems (GIS) was held in November of 2012. In conjunction with this conference, we organized the conference's first competition, called the SIGSPATIAL GIS Cup 2012. The subject of the competition was map matching, which is the problem of correctly matching a sequence of noisy GPS points to roads. We describe the details of the contest, the results of the competition, and the lessons we learned in running a contest like this.

Instead of traditional ways of creating road maps, an attractive alternative is to create a map based on GPS traces of regular drivers. One important aspect of this approach is to automatically compute the number and locations of driving lanes on a road. We introduce the idea of using a Gaussian mixture model (GMM) to model the distribution of GPS traces across multiple traffic lanes. The GMM naturally accounts for the inherent spread in GPS data. We present a new variation of the GMM that enforces constant lane width and GPS variance in each lane. For fitting the GMM, we also introduce a new regularizer that is sensitive to the overall spread of the GPS data across the road. Our experiments on real GPS data show that our new GMM is better at counting lanes than a more traditional GMM, and it gives more consistent results across our data set.

This paper presents a method for automatically converting raw GPS traces from everyday vehicles into a routable road network. The method begins by smoothing raw GPS traces using a novel aggregation technique. This technique pulls together traces that belong on the same road in response to simulated potential energy wells created around each trace. After the traces are moved in response to the potential fields, they tend to coalesce into smooth paths. To help adjust the parameters of the constituent potential fields, we present a theoretical analysis of the behavior of our algorithm on a few different road configurations. With the resulting smooth traces, we apply a custom clustering algorithm to create a graph of nodes and edges representing the road network. We show how this network can be used to plan reasonable driving routes, much like consumer-oriented mapping Web sites. We demonstrate our algorithms using real GPS data collected on public roads, and we evaluate the effectiveness of our approach on public roads, and we evaluate the effectiveness of our approach by comparing the route planning results suggested by our generated graph to a commercial route planner.

The problem of matching measured latitude/longitude points to roads is becoming increasingly important. This paper describes a novel, principled map matching algorithm that uses a Hidden Markov Model (HMM) to find the most likely road route represented by a time-stamped sequence of latitude/longitude pairs. The HMM elegantly accounts for measurement noise and the layout of the road network. We test our algorithm on ground truth data collected from a GPS receiver in a vehicle. Our test shows how the algorithm breaks down as the sampling rate of the GPS is reduced. We also test the effect of increasing amounts of additional measurement noise in order to assess how well our algorithm could deal with the inaccuracies of other location measurement systems, such as those based on WiFi and cell tower multilateration. We provide our GPS data and road network representation as a standard test set for other researchers to use in their map matching work.