1,213 research outputs found
On the Inability of Markov Models to Capture Criticality in Human Mobility
We examine the non-Markovian nature of human mobility by exposing the
inability of Markov models to capture criticality in human mobility. In
particular, the assumed Markovian nature of mobility was used to establish a
theoretical upper bound on the predictability of human mobility (expressed as a
minimum error probability limit), based on temporally correlated entropy. Since
its inception, this bound has been widely used and empirically validated using
Markov chains. We show that recurrent-neural architectures can achieve
significantly higher predictability, surpassing this widely used upper bound.
In order to explain this anomaly, we shed light on several underlying
assumptions in previous research works that has resulted in this bias. By
evaluating the mobility predictability on real-world datasets, we show that
human mobility exhibits scale-invariant long-range correlations, bearing
similarity to a power-law decay. This is in contrast to the initial assumption
that human mobility follows an exponential decay. This assumption of
exponential decay coupled with Lempel-Ziv compression in computing Fano's
inequality has led to an inaccurate estimation of the predictability upper
bound. We show that this approach inflates the entropy, consequently lowering
the upper bound on human mobility predictability. We finally highlight that
this approach tends to overlook long-range correlations in human mobility. This
explains why recurrent-neural architectures that are designed to handle
long-range structural correlations surpass the previously computed upper bound
on mobility predictability
Modeling, Predicting and Capturing Human Mobility
Realistic models of human mobility are critical for modern day applications, specifically for recommendation systems, resource planning and process optimization domains. Given the rapid proliferation of mobile devices equipped with Internet connectivity and GPS functionality today, aggregating large sums of individual geolocation data is feasible. The thesis focuses on methodologies to facilitate data-driven mobility modeling by drawing parallels between the inherent nature of mobility trajectories, statistical physics and information theory. On the applied side, the thesis contributions lie in leveraging the formulated mobility models to construct prediction workflows by adopting a privacy-by-design perspective. This enables end users to derive utility from location-based services while preserving their location privacy. Finally, the thesis presents several approaches to generate large-scale synthetic mobility datasets by applying machine learning approaches to facilitate experimental reproducibility
Predicting encounter and colocation events
Although an extensive literature has been devoted to mine and model mobility features, forecasting where, when and whom people will encounter/colocate still deserve further research effort s. Forecasting people\u2019s encounter and colocation features is the key point for the success of many applications rang- ing from epidemiology to the design of new networking paradigms and services such as delay tolerant and opportunistic networks. While many algorithms which rely on both mobility and social informa- tion have been proposed, we propose a novel encounter and colocation predictive model which predicts user\u2019s encounter and colocation events and their features by exploiting the spatio-temporal regularity in the history of these events. We adopt a weighted features Bayesian predictor and evaluate its accuracy on two large scales WiFi and cellular datasets. Results show that our approach could improve prediction accuracy with respect to standard na\uefve Bayesian and some of the state of the art predictors
Context-aware multi-head self-attentional neural network model for next location prediction
Accurate activity location prediction is a crucial component of many mobility
applications and is particularly required to develop personalized, sustainable
transportation systems. Despite the widespread adoption of deep learning
models, next location prediction models lack a comprehensive discussion and
integration of mobility-related spatio-temporal contexts. Here, we utilize a
multi-head self-attentional (MHSA) neural network that learns location
transition patterns from historical location visits, their visit time and
activity duration, as well as their surrounding land use functions, to infer an
individual's next location. Specifically, we adopt point-of-interest data and
latent Dirichlet allocation for representing locations' land use contexts at
multiple spatial scales, generate embedding vectors of the spatio-temporal
features, and learn to predict the next location with an MHSA network. Through
experiments on two large-scale GNSS tracking datasets, we demonstrate that the
proposed model outperforms other state-of-the-art prediction models, and reveal
the contribution of various spatio-temporal contexts to the model's
performance. Moreover, we find that the model trained on population data
achieves higher prediction performance with fewer parameters than
individual-level models due to learning from collective movement patterns. We
also reveal mobility conducted in the recent past and one week before has the
largest influence on the current prediction, showing that learning from a
subset of the historical mobility is sufficient to obtain an accurate location
prediction result. We believe that the proposed model is vital for
context-aware mobility prediction. The gained insights will help to understand
location prediction models and promote their implementation for mobility
applications.Comment: updated Discussion section; accepted by Transportation Research Part
Activity-aware Human Mobility Prediction with Hierarchical Graph Attention Recurrent Network
Human mobility prediction is a fundamental task essential for various
applications, including urban planning, location-based services and intelligent
transportation systems. Existing methods often ignore activity information
crucial for reasoning human preferences and routines, or adopt a simplified
representation of the dependencies between time, activities and locations. To
address these issues, we present Hierarchical Graph Attention Recurrent Network
(HGARN) for human mobility prediction. Specifically, we construct a
hierarchical graph based on all users' history mobility records and employ a
Hierarchical Graph Attention Module to capture complex time-activity-location
dependencies. This way, HGARN can learn representations with rich human travel
semantics to model user preferences at the global level. We also propose a
model-agnostic history-enhanced confidence (MAHEC) label to focus our model on
each user's individual-level preferences. Finally, we introduce a Temporal
Module, which employs recurrent structures to jointly predict users' next
activities (as an auxiliary task) and their associated locations. By leveraging
the predicted future user activity features through a hierarchical and residual
design, the accuracy of the location predictions can be further enhanced. For
model evaluation, we test the performances of our HGARN against existing SOTAs
in both the recurring and explorative settings. The recurring setting focuses
on assessing models' capabilities to capture users' individual-level
preferences, while the results in the explorative setting tend to reflect the
power of different models to learn users' global-level preferences. Overall,
our model outperforms other baselines significantly in all settings based on
two real-world human mobility data benchmarks. Source codes of HGARN are
available at https://github.com/YihongT/HGARN.Comment: 11 page
Mobile Crowd Location Prediction with Hybrid Features using Ensemble Learning
With the explosive growth of location-based service on mobile devices, predicting usersâ future locations and trajectories is of increasing importance to support proactive information services. In this paper, we model this problem as a supervised learning task and propose to use ensemble learning methods with hybrid features to solve it. We characterize the properties of usersâ visited locations and movement patterns and then extract feature types (temporal, spatial, and system) to quantify the correlation between locations and features. Finally, we apply ensemble methods to predict usersâ future locations with extracted features. Moreover, we design an adaptive Markov Chain model to predict usersâ trajectories between two locations. To evaluate the system performance, we use a real-life dataset from the Nokia Mobile Data Challenge. Experiment results unveil interesting findings: (1) For individual predictors, Bayes Networks outperform all others when data quality is good, while J48 delivers the best results when data quality is bad; (2) Ensemble predictors outperform individual predictors in general under all conditions; and (3) Ensemble predictor performance depends on the user movement patterns
Mobile Crowd Location Prediction with Hybrid Features using Ensemble Learning
With the explosive growth of location-based service on mobile devices, predicting usersâ future locations and trajectories is of increasing importance to support proactive information services. In this paper, we model this problem as a supervised learning task and propose to use ensemble learning methods with hybrid features to solve it. We characterize the properties of usersâ visited locations and movement patterns and then extract feature types (temporal, spatial, and system) to quantify the correlation between locations and features. Finally, we apply ensemble methods to predict usersâ future locations with extracted features. Moreover, we design an adaptive Markov Chain model to predict usersâ trajectories between two locations. To evaluate the system performance, we use a real-life dataset from the Nokia Mobile Data Challenge. Experiment results unveil interesting findings: (1) For individual predictors, Bayes Networks outperform all others when data quality is good, while J48 delivers the best results when data quality is bad; (2) Ensemble predictors outperform individual predictors in general under all conditions; and (3) Ensemble predictor performance depends on the user movement patterns
Deep Learning Techniques for Mobility Prediction and Management in Mobile Networks
Trajectory prediction is an important research topic in modern mobile networks (e.g., 5G and beyond 5G) to enhance the network quality of service by accurately predicting the future locations of mobile users, such as pedestrians and vehicles, based on their past mobility patterns. A trajectory is defined as the sequence of locations the user visits over time.
The primary objective of this thesis is to improve the modeling of mobility data and establish personalized, scalable, collective-intelligent, distributed, and strategic trajectory prediction techniques that can effectively adapt to the dynamics of urban environments in order to facilitate the optimal delivery of mobility-aware network services. Our proposed approaches aim to increase the accuracy of trajectory prediction while minimizing communication and computational costs leading to more efficient mobile networks.
The thesis begins by introducing a personalized trajectory prediction technique using deep learning and reinforcement learning. It adapts the neural network architecture to capture the distinct characteristics of mobile usersâ data. Furthermore, it introduces advanced anticipatory handover management and dynamic service migration techniques that optimize network management using our high-performance trajectory predictor. This approach ensures seamless connectivity and proactively migrates network services, enhancing the quality of service in dense wireless networks.
The second contribution of the thesis introduces cluster-level prediction to extend the reinforcement learning-based trajectory prediction, addressing scalability challenges in large-scale networks. Cluster-level trajectory prediction leverages usersâ similarities within clusters to train only a few representatives. This enables efficient transfer learning of pre-trained mobility models and reduces computational overhead enhancing the network scalability.
The third contribution proposes a collaborative social-aware multi-agent trajectory prediction technique that accounts for the interactions between multiple intra-cluster agents in a dynamic urban environment, increasing the prediction accuracy but decreasing the algorithm complexity and computational resource usage.
The fourth contribution proposes a federated learning-driven multi-agent trajectory prediction technique that leverages the collaborative power of multiple local data sources in a decentralized manner to enhance user privacy and improve the accuracy of trajectory prediction while jointly minimizing computational and communication costs.
The fifth contribution proposes a game theoretic non-cooperative multi-agent prediction technique that considers the strategic behaviors among competitive inter-cluster mobile users.
The proposed approaches are evaluated on small-scale and large-scale location-based mobility datasets, where locations could be GPS coordinates or cellular base station IDs. Our experiments demonstrate that our proposed approaches outperform state-of-the-art trajectory prediction methods making significant contributions to the field of mobile networks
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