User Movement Trajectories Research Articles

Mobility Markov Chain and Matrix-Based Location-Aware Cache Replacement Policy in Mobile Environment

In the location-aware services, past mobile device cache invalidation-replacement practises used are ineffective if the client travel route varies rapidly. In addition, in terms of storage expense, previous cache invalidation-replacement policies indicate high storage overhead. These limitations of past policies are inspiration for this research work. The paper describes the models to solve the aforementioned challenges using two different approaches separately for predicting the future path for the user movement. In the first approach, the most prevalent sequential pattern mining and clustering (SPMC) technique is used to pre-process the user's movement trajectory and find out the pattern that appears frequently. In the second approach, frequent patterns are forwarded into the mobility Markov chain and matrix (MMCM) algorithm leading to a reduction in the size of candidate sets and, therefore, efficiency enhancement of mining sequence patterns. Analytical results show significant caching performance improvement compared to previous caching policies.

Investigating the Complexity of Spatial Interactions between Different Administrative Units in China Using Flickr Data

Location-based social media have facilitated us to bridge the gap between virtual and physical worlds through the exploration of human online dynamics from a geographic perspective. This study uses a large collection of geotagged photos from Flickr to investigate the complexity of spatial interactions at the country level. We adopted three levels of administrative divisions in mainland China—province, city, and county—as basic geographic units and established three types of topology—province–province network, city–city network, and county–county network—from the extracted user movement trajectories. We conducted the scaling analysis based on heavy-tailed distribution statistics including power law exponents, goodness of fit index, and ht-index, by which we characterized a great complexity of the trajectory lengths, spatial distribution of geotagged photos, and the related metrics of built networks. The great complexity indicates the highly imbalanced ratio of populated-to-unpopulated areas or large-to-small flows between areas. More interestingly, all power law exponents were around 2 for the networks at various spatial and temporal scales. Such a recurrence of scaling statistics at multiple resolutions can be regarded a statistical self-similarity and could thus help us to reveal the fractal nature of human mobility patterns.

Exploring the heterogeneity of human urban movements using geo-tagged tweets

ABSTRACT The availability of vast amounts of location-based data from social media platforms such as Twitter has enabled us to look deeply into the dynamics of human movement. The aim of this paper is to leverage a large collection of geo-tagged tweets and the street networks of two major metropolitan areas—London and Tokyo—to explore the underlying mechanism that determines the heterogeneity of human mobility patterns. For the two target cities, hundreds of thousands of tweet locations and road segments were processed to generate city hotspots and natural streets. User movement trajectories and city hotspots were then used to build a hotspot network capable of quantitatively characterizing the heterogeneous movement patterns of people within the cities. To emulate observed movement patterns, the study conducts a two-level agent-based simulation that includes random walks through the hotspot networks and movements in the street networks using each of three distance types—metric, angular and combined. Comparisons of the simulated and observed movement flows at the segment and street levels show that the heterogeneity of human urban movements at the collective level is mainly shaped by the scaling structure of the urban space.

A Mobile Channel Model for VLC and Application to Adaptive System Design

In this letter, we propose a realistic channel model for visible light communication (VLC) assuming a mobile user. Based on non-sequential ray tracing, we first obtain channel impulse responses for each point over the user movement trajectories, and then express path loss and delay spread as a function of distance through curve fitting. Our results demonstrate large variations in received power. In system design, this necessitates the use of adaptive schemes, where transmission parameters can be selected according to channel conditions. To demonstrate the benefits of link adaptation over a mobile VLC channel, we propose an adaptive system with luminary selection and demonstrate improvements in spectral efficiency over non-adaptive systems.

Social media as sensor in real world: movement trajectory detection with microblog

Location information is very important in the real world. How to detect users' locations and further movement trajectories automatically is significant for many location-based services such as dietary recommendation and tourism planning. With the rapid development of social media such as Sina Weibo and Twitter, more and more people publish posts at any time which contain their real-time location information. This makes it possible to detect users' movement trajectories automatically by social media. In this paper, we propose a novel method to detect city-level locations and further identify movement trajectory for a given user in social media. Our efforts are made in two phases: (1) location detection, and (2) movement trajectory identification. Phase (1) combines two components, i.e. a Bayes model based on words distribution over locations and the social relationships of users in social media, to detect or infer users' locations. The former one is utilized to judge whether the content of a post contains explicit/implicit location information and further to detect the location. The latter one assumes that friends tend to gather together when they talk about the same thing. Thus, it infers a user's locations from his friends' detected locations in social media. Phase (2) constructs the user's movement trajectory by smoothing these detected locations in first phase according to both semantic context of posts and transfer time between locations. Experiments on real dataset from Sina Weibo demonstrate that our approach can outperform baseline methods significantly in terms of Precision, Recall, F1, Error Distance (ErrDist), Detect Ratio (DeteRatio), T-Precision, T-Recall and T-F1, respectively.

An experimental characterization of reservoir computing in ambient assisted living applications

In this paper, we present an introduction and critical experimental evaluation of a reservoir computing (RC) approach for ambient assisted living (AAL) applications. Such an empirical analysis jointly addresses the issues of efficiency, by analyzing different system configurations toward the embedding into computationally constrained wireless sensor devices, and of efficacy, by analyzing the predictive performance on real-world applications. First, the approach is assessed on a validation scheme where training, validation and test data are sampled in homogeneous ambient conditions, i.e., from the same set of rooms. Then, it is introduced an external test set involving a new setting, i.e., a novel ambient, which was not available in the first phase of model training and validation. The specific test-bed considered in the paper allows us to investigate the capability of the RC approach to discriminate among user movement trajectories from received signal strength indicator sensor signals. This capability can be exploited in various AAL applications targeted at learning user indoor habits, such as in the proposed indoor movement forecasting task. Such a joint analysis of the efficiency/efficacy trade-off provides novel insight in the concrete successful exploitation of RC for AAL tasks and for their distributed implementation into wireless sensor networks.

A regression-based approach for mining user movement patterns from random sample data

Mobile computing systems usually express a user movement trajectory as a sequence of areas that capture the user movement trace. Given a set of user movement trajectories, user movement patterns refer to the sequences of areas through which a user frequently travels. In an attempt to obtain user movement patterns for mobile applications, prior studies explore the problem of mining user movement patterns from the movement logs of mobile users. These movement logs generate a data record whenever a mobile user crosses base station coverage areas. However, this type of movement log does not exist in the system and thus generates extra overheads. By exploiting an existing log, namely, call detail records, this article proposes a Regression-based approach for mining User Movement Patterns (abbreviated as RUMP). This approach views call detail records as random sample trajectory data, and thus, user movement patterns are represented as movement functions in this article. We propose algorithm LS (standing for Large Sequence) to extract the call detail records that capture frequent user movement behaviors. By exploring the spatio-temporal locality of continuous movements (i.e., a mobile user is likely to be in nearby areas if the time interval between consecutive calls is small), we develop algorithm TC (standing for Time Clustering) to cluster call detail records. Then, by utilizing regression analysis, we develop algorithm MF (standing for Movement Function) to derive movement functions. Experimental studies involving both synthetic and real datasets show that RUMP is able to derive user movement functions close to the frequent movement behaviors of mobile users.

Learning Trajectory Information with Neural Networks and the Markov Model to Develop Intelligent Location-Based Services

In the development of location-based services, various location-sensing techniques and experimental/commercial services have been used. However, conventional location-based services are limited in terms of flexibility because they depend on the current location of the user. We propose a novel method of predicting the user's future movements in order to develop advanced location-based services. The user's movement trajectory is modelled using a combination of recurrent self-organising maps (RSOM) and the Markov model. Future movement is predicted based on past movement trajectories. A prototype application based on location prediction is also presented. This application is a mobile user assistant targeted to university students. To verify the proposed method, a GPS dataset was collected on the Yonsei University campus. The results were promising enough to confirm that the application works flexibly even in ambiguous situations.