User Movement Patterns Research Articles

Collaborative Geodesign for Alternative-Fuel Station Location using “Collablocation” Software

Adoption of alternative-fuel vehicles requires a network of conveniently located fuel stations capable of serving the movement patterns of potential users. One hindrance to planning an effective network of stations is the difficulty of integrating the competing views of multiple stakeholders into an agreeable solution. This paper reports on a collaborative geodesign methodology applied to the problem of compressed natural gas fueling locations in the southwestern U.S.A. Geodesign is a multi-stakeholder spatial planning process that has gained currency in the GIS community but has not been applied to fuel infrastructure previously. We have developed an open-source interactive geovisual platform called Collablocation and a structured group process to facilitate interactive exploration of scenarios with multiple spatial data layers and to permit real-time computation and evaluation of network performance characteristics. For the pilot workshop we recruited expert stakeholders from industry, government, and local organizations. Breakout groups reached convergence on six locations on the I-10 and I-80, and at the Arizona–Mexico border, and several other less precise areas of need were identified. A post-workshop survey indicated high satisfaction with the technical features of the platform and the workshop design, and highlighted the ease of use and exchange of information.

Mobile crowd location prediction with hybrid features using ensemble learning

With the explosive growth of location-based service on mobile devices, predicting users’ 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 users’ 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 users’ future locations with extracted features. Moreover, we design an adaptive Markov Chain model to predict users’ 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.

Reducing the Frequent Switch Over between the Base Stations through Continuous Analysis of Signal Strength

As the increase in the number of mobiles and mobile networks, the existing standards and technologies face several issues and challenges. In the mobility management of cellular networks, HO is the important parameter on which these issues and challenges affect drastically. Several research papers have proposed many ideas and schemes to reduce HO latency. As the increase in the number new technologies and networks, the existing ideas are no longer supported efficiently. So a continuous analysis on handover (HO) procedure is required. Signal strength (SS) and quality of service (QoS) are measured at particular time intervals in cellular networks. Results show that the highest value of signal strength has the best quality of service. In this paper, a continuous analysis on signal strength received by the mobile station (MS) is used to monitor the HO process to reduce the HO delay. In this analysis, the parameters user movement pattern, topological position, signal strength and time are used which will be helpful to forecast the HO in advance. Using HO forecast minimum number of frequent switch over between the base stations (BSs) reduces the HO latency in mobile networks.

Smart prediction for seamless mobility in F-HMIPv6 based on location based services

This paper proposed a new scheme for smooth handoff over F-HMIPv6 networks based on Location Based Services (LBS). It uses the available information about mobile node (MN) such as user mobility patterns and MN’s velocity to reduce handoff latency. In the proposed scheme, the movement pattern of users plays an important role in the performance analysis. The proposed scheme develops LBS that records a number of mobility patterns based on user daily behavior, and exploits these information to reduce the handoff latency. The proposed scheme is analyzed and compared with F-HMIPv6 protocol. The results show that it improves the performance in terms of handoff latency, packet delivery cost, and location update cost. Specifically, the proposed scheme achieves the tradeoff between realizing a smooth handoff and minimizing the cost that is the demand for all customers of mobile services.

Personalized location prediction for group travellers from spatial–temporal trajectories

In recent years, research on location predictions by mining trajectories of users has attracted a lot of attentions. Existing studies on this topic mostly focus on individual movements, considering the trajectories as solo movements. However, a user usually does not visit locations just for the personal interest. The preference of a travel group has significant impacts on the places they visit. In this paper, we propose a novel personalized location prediction approach which further takes into account users’ travel group type. To achieve this goal, we propose a new group pattern discovery approach to extract the travel groups from spatial–temporal trajectories of users. Type of the discovered groups, then, are identified through utilizing the profile information of the group members. The core idea underlying our proposal is the discovery of significant movement patterns of users to capture frequent movements by considering the group types. Finally, the problem of location prediction is formulated as an estimation of the probability of a given user visiting a given location based on his/her current movement and his/her group type. To the best of our knowledge, this is the first work on location prediction based on trajectory pattern mining that investigates the influence of travel group type. By means of a comprehensive evaluation using various datasets, we show that our proposed location prediction framework achieves significantly higher performance than previous location prediction methods.

Complete User Mobility via User and Trajectory Embeddings

Nowadays, human movements are recorded with a variety of tools, forming different trajectorysets which are usually isolated from each other. These isolated trajectories come from different tools such as Twitter, Facebook, and so on that are related to one individual. Thus, it is important to link trajectories to users who generate them in order to provide massive information for facilitating trajectory mining tasks. Most previous work took advantage of trajectory similarities or trajectory classification models to solve this problem. However, these methods ignored the users’ movement patterns, which play a critical role in mining information from these users’ trajectories. In this paper, we propose a novel approach called trajectory linking via user embedding and trajectory embedding to learn the movement patterns of users and trajectories simultaneously, and use those movement patterns to link trajectories to users. More specifically, we leverage a graph-based location embedding method to learn the semantics of locations based on categorical and spatial information of locations. Then, a novel dual-objective neural network model is designed to integrate the sequential dependency and temporal regularity of trajectories to learn the movement patterns of users and their trajectories at the same time. A trajectory is then linked to the user who has the most similar movement pattern with it. Moreover, the advantages of our approach are empirically verified on real-world public trajectory datasets with convincing results.

CMS의 사용자 로그를 이용한 패턴 분석 방법 연구

급속히 발전하고 있는 현대사회에서는 다양한 스마트 디바이스의 출현으로 원하는 정보의 획득이 용이해졌으며, 웹을 기반으로 하는 콘텐츠를 이용하는 빈도가 급속히 성장하고 있다. 또한 기업들은 웹 기반 하에서 CMS를 이용하여 콘텐츠를 생산, 수정하는 형태로 바뀌고 있고, 사용자에게 콘텐츠를 제공하는데 있어 매우 중요한 부분이라 할 수 있다. 현재 웹 서비스는 각종 디바이스에 맞게 UI를 설계하고 제공하고 있으며, 이용 편이성을 향상하기 위하여 설문 조사 및 전체 사용자 패턴을 분석하여 서비스를 개선하고 있는 실정이다. 즉 UX를 고려하지 않고 기술적인 측면에서 설계되는 경향이 대부분이라 할 수 있다. 이에 본 논문에서는 전체 사용자의 패턴 분석으로 웹 서비스에 적용하는 한계를 탈피하여 개인 사용자의 이동 패턴 및 사용 패턴을 기반으로 애니메이션을 통하여 비주얼화 하는 시스템을 제안한다. 이러한 융복합을 통하여 웹 관리자 및 기획연구자 측면에서 UX를 사용자 중심으로 변화시킬 수 있을 것으로 기대된다. In modern society, which boomed it became easier to obtain the necessary information to the emergence of a variety of smart devices. Due to this, the frequency of using the content based on the Web is growing rapidly. In addition, companies are turning into a production and modify the content using the CMS under the web-based. It can be a very important part to provide users with the content. Currently web services are designing a UI to the device and provided. To improve the ease of use, they are enhancing services only by survey and analysis of the patterns of all users. Most are designed without considering the UX only in the technical aspects. In this paper, to break the limits that apply to all users of the Web service pattern analysis, we propose a visualization system via the animation based on the individual user's movement patterns and usage patterns. Through this convergence is expected to be able to transform the web from the central manager to the user UX and the planning aspects researchers.

Direct Connection on the Move: Characterization of User Mobility in Cellular-Assisted D2D Systems

Next-generation device-to-device (D2D) communication technology, in which a cellular network assists proximal users at all stages of their interaction, is rapidly developing. Previous research has thoroughly characterized D2D performance aspects from discovery to connection establishment, security, and service continuity; however, prospective D2Denabled applications and services envision highly opportunistic device contacts as a consequence of unpredictable human user mobility. Therefore, mobility's impact on D2D communication requires careful investigation to understand the practical operational efficiency of future cellular-assisted D2D systems. This article offers a first-hand tutorial on various implications of D2D mobility across different user movement patterns and mobility-related parameters and proposes an assessment methodology for D2D-enabled systems. The rigorous system-level evaluation conducted for this study delivers important conclusions on the effects of user mobility in emerging D2D-centric systems.

A model to estimate and interpret the energy-efficiency of movement patterns in urban road traffic

Urban road traffic is highly dynamic. Traffic conditions vary in time and with location and so do the movement patterns of individual road users. In this article, a movement pattern is the behaviour of a car when traversing a road link in an urban road network. A movement pattern can be recorded with a global navigation satellite system (GNSS), such as the Global Positioning System (GPS). A movement pattern has a specific energy-efficiency, which is a measure of how fuel-intensively the car is moving. For example, a car driving uniformly at medium speed consumes little fuel and, therefore, is energy-efficient, whereas stop-and-go driving consumes much fuel and is energy-inefficient. In this article we introduce a model to estimate the energy-efficiency of movement patterns in urban road traffic from GNSS data. First, we derived statistical features about the car's movement along the road. Then, we compared these to fuel consumption data from the car's controller area network (CAN) bus, normalized to the car's overall range of fuel consumption. We identified the optimal feature set for prediction. With the optimal feature set we trained, tested and verified a model to estimate energy-efficiency, with the fuel consumption serving as ground truth. Existing fuel consumption models usually view movement as a snapshot. Thus, the behaviour of the car remains unknown that causes a movement pattern to be energy-efficient or energy-inefficient. Our model views movement as a process and allows to interpret this process. A movement pattern can, for example, be energy-inefficient because the car is driving in stop-and-go traffic, because it is travelling at high speed, or because it is accelerating. Our model allows to distinguish between these different types of behaviours. Thus, it can provide new insights into the dynamics of urban road traffic and its energy-efficiency.

Spatial Analysis of Users Movement Pattern and its Socialization on Public Facilities and Environment through the ESVA

The aim of developing spatial analysis of user movement pattern analysis and socialization is to ensure socializing comfort at the user environment. User Movement patterns and socialization need to be estimated in order to avoid any disturbance in obtaining user comfort during in its environment. Research on this spatial analysis was held on public facilities in Surabaya and Malang City. In the analysis on various public facilities was concluded that comfort sense was influenced by facilities and infrastructure, spacious place to socialize, view and direction in order to control the circulation flow and the ability to accept a variety of activities that are likely arise. Results of this research showed that the type of movement and socialization can be known through the Environment Socialization Values Analysis (ESVA). The value results of the ESVA are determined from the average number of socialization types that occurs and the average of user member number.

HeadsUp: Keeping Pedestrian Phone Addicts from Dangers Using Mobile Phone Sensors

Walking while staring at the mobile phone is dangerous, and the danger mainly arises from distraction. While watching the mobile phone, one could fall into a deep well without noticing the manhole cover was missing, one could be hit by a rushing car without observing the traffic light, and so forth. Some mobile phone users are already aware of the crisis, and they keep looking up and down to allocate some focus to danger spying; however, the statistics data revealed by US government make such efforts frustrating: about 1,152 pedestrians are injured in US in the year 2010, while they were using mobile phones, and the number doubled in the year 2012. This paper identified the possibility of using mobile phone sensors to develop a walk pattern recognition system. By sampling from embedded sensor, such as accelerometer and gyroscope, the movement pattern of mobile phone users can be computed. We design and implement HeadsUp, a system that warns pedestrian and locks the screen when one looks at the mobile phone while walking. Evaluation results from experiments of 20 testers in real life situation show that, on average, the false negative rate is less than 3%.

타임라인데이터를 이용한 트위터 사용자의 거주 지역 유추방법

SNS사용자의 거주 지역을 유추하여 그들이 생성한 데이터에 거주위치를 부여하는 것은 위치희박(location sparsity)과 생태학적 오류문제로 인해 연구결과의 신뢰성이 떨어진다는 평가를 받아온 공간빅데이터 연구에 대안이 될 수 있다. 본 연구에서는 Tweet 사용자의 거주 지역을 유추하는 방법으로 사용자 타임라인데이터 속에서 찾아낸 일상생활활동패턴을 이용하는 방법을 고안하였다. 트윗 사용자의 일상생활활동패턴은 이동궤적과 사용자의 언어(text)에서 확인할 수 있었으며 전자를 활용한 모델을 일상이동패턴모델, 후자를 활용한 모델을 일상 활동장 모델이라 명명하고 각각 모델에 입력될 변수를 선정하였다. 자신의 거주 지역에서 가장 높은 빈도의 트윗 발생 여부와 가장 높은 빈도의 거주행정구역 표현 단어를 사용하는지 아닌지를 종속변수로 한 판별분석을 실시하여 모델을 작성하였으며 설명력은 일상 이동패턴모델, 일상 활동장 모델 각각 67.5%, 57.5%였다. 이 모델을 스트레스 관련 트윗을 작성한 사용자의 타임라인데이터로 구성된 테스트데이터에 입력해본 결과 전체 사용자 48,235명 중 5,301명의 거주 지역을 유추하였고 이를 활용하여 위치 부여된 스트레스 관련 트윗 9,606개를 확보하였다. 본 연구의 유추기법을 통해 기존 SNS데이터 분석연구에서 사용하는 데이터 수집 방법보다 44배 많은 위치 부여 트윗을 확보할 수 있었다. 본 연구방법론은 SNS데이터를 이용한 연구에서 위치 부여된 데이터를 확보하는데 활용 가능할 것으로 판단되며, 각종 지역통계와 상관관계파악을 통해 지역적 현상 분석에도 SNS데이터를 이용할 수 있는 가능성을 높일 것으로 판단된다. If one can infer the residential area of SNS users by analyzing the SNS big data, it can be an alternative by replacing the spatial big data researches which result from the location sparsity and ecological error. In this study, we developed the way of utilizing the daily life activity pattern, which can be found from timeline data of tweet users, to infer the residential areas of tweet users. We recognized the daily life activity pattern of tweet users from user's movement pattern and the regional cognition words that users text in tweet. The models based on user's movement and text are named as the daily movement pattern model and the daily activity field model, respectively. And then we selected the variables which are going to be utilized in each model. We defined the dependent variables as 0, if the residential areas that users tweet mainly are their home location(HL) and as 1, vice versa. According to our results, performed by the discriminant analysis, the hit ratio of the two models was 67.5%, 57.5% respectively. We tested both models by using the timeline data of the stress-related tweets. As a result, we inferred the residential areas of 5,301 users out of 48,235 users and could obtain 9,606 stress-related tweets with residential area. The results shows about 44 times increase by comparing to the geo-tagged tweets counts. We think that the methodology we have used in this study can be used not only to secure more location data in the study of SNS big data, but also to link the SNS big data with regional statistics in order to analyze the regional phenomenon.

Attack-Resilient Mix-zones over Road Networks: Architecture and Algorithms

Continuous exposure of location information, even with spatially cloaked resolution, may lead to breaches of location privacy due to statistics-based inference attacks. An alternative and complementary approach to spatial cloaking based location anonymization is to break the continuity of location exposure by introducing techniques, such as mix-zones, where no application can trace user movements. Several factors impact on the effectiveness of mix-zone approach, such as user population, mix-zone geometry, location sensing rate and spatial resolution, as well as spatial and temporal constraints on user movement patterns. However, most of the existing mix-zone proposals fail to provide effective mix-zone construction and placement algorithms that are resilient to timing and transition attacks. This paper presents MobiMix, a road network based mix-zone framework to protect location privacy of mobile users traveling on road networks. It makes three original contributions. First, we provide the formal analysis on the vulnerabilities of directly applying theoretical rectangle mix-zones to road networks in terms of anonymization effectiveness and resilience to timing and transition attacks. Second, we develop a suite of road network mix-zone construction methods that effectively consider the above mentioned factors to provide higher level of resilience to timing and transition attacks, and yield a specified lowerbound on the level of anonymity. Third, we present a set of mix-zone placement algorithms that identify the best set of road intersections for mix-zone placement considering the road network topology, user mobility patterns and road characteristics. We evaluate the MobiMix approach through extensive experiments conducted on traces produced by GTMobiSim on different scales of geographic maps. Our experiments show that MobiMix offers high level of anonymity and high level of resilience to timing and transition attacks, compared to existing mix-zone approaches.

SCPS: A Social-Aware Distributed Cyber-Physical Human-Centric Search Engine

Advances in ubiquitous sensing, computing and wireless communication technologies are leading to the development of cyber-physical systems (CPS), which promise to revolutionize the way we interact with the physical world. CPS applications, such as healthcare monitoring, may involve many users and objects scattered over a wide area. One critical function of CPS is object search in the physical world through the cyber sphere that enables interaction between the cyber and physical spheres. Some of the previously proposed physical object search engines use RFID tracking, and others collect the information of object locations into a hierarchical centralized server. The difficulty of widely deploying RFID devices, the centralized search, and the need for periodical location information collection prevent CPS from achieving higher scalability and efficiency. To deal with this problem, we propose a Social-aware distributed Cyber-Physical human-centric Search engine (SCPS) that leverages the social network formed by wireless device users for object search. Without requiring periodical location information collection, SCPS locates objects held by users based on the routine user movement pattern. Moreover, using a social-aware Bayesian network, it can accurately predict the users’ locations even at the occurrence of exceptional (i.e., non-routine) events (e.g., raining) that break user movement pattern. Thus, SCPS is more advantageous than all previous social network based works which assume that user behaviors always follow a certain pattern. Further, SCPS conducts the search in a fully distributed manner by relying on a distributed hash table (DHT) structure. As a result, SCPS achieves high scalability, efficiency and location accuracy. Extensive real-trace driven simulation results show the superior performance of SCPS compared to other representative search methods including a hierarchical centralized method, a decentralized method, and two social network based methods. The results also show the effectiveness of different components of SCPS.

Support Vector Machine Based Mobility Prediction Scheme in Heterogeneous Wireless Networks

To improve the intelligence of the mobile-aware applications in the heterogeneous wireless networks (HetNets), it is essential to establish an advanced mechanism to anticipate the change of the user location in every subnet for HetNets. This paper proposes a multiclass support vector machine based mobility prediction (Multi-SVMMP) scheme to estimate the future location of mobile users according to the movement history information of each user in HetNets. In the location prediction process, the regular and random user movement patterns are treated differently, which can reflect the user movements more realistically than the existing movement models in HetNets. And different forms of multiclass support vector machines are embedded in the two mobility patterns according to the different characteristics of the two mobility patterns. Moreover, the introduction of target region (TR) cuts down the energy consumption efficiently without impacting the prediction accuracy. As reported in the simulations, our Multi-SVMMP can overcome the difficulties found in the traditional methods and obtain a higher prediction accuracy and user adaptability while reducing the cost of prediction resources.

A Cached Registration Scheme for IP Multimedia Subsystem (IMS)

IP Multimedia Subsystem (IMS), an architectural framework for delivering multimedia services, was standardized by 3GPP/3GPP2. It is integrated with 4G and will most likely be with 5G and beyond, so as to enable wireless carriers to provide rich multimedia services, such as IPTV, chat, push to talk, and video conference. To use these services, users need to perform registration procedure with IMS, which will provide user information to the system. Registration is also performed when users move from one network to another. Due to the complexity of IMS and the increasing demands for these services, a key challenge IMS faces is to provide QoS to meet user requirements. One of the key performance factors is delay encountered in registration and service establishment. Also, users’ mobility impacts the rate of registration and consequently the signaling generated that must be handled by the system. This work proposes and analyzes a cached registration scheme to reduce the delay associated with registration. The work also examines the effects of different mobility models on the application layer, in particular IMS. The work studies the impact of user movement patterns on the system and examines the impact of the proposed cached registration scheme on these patterns.

Insensitivity of User Distribution in Multicell Networks under General Mobility and Session Patterns

The location of active users is an important factor in the performance analysis of mobile multicell networks, but it is difficult to quantify due to the wide variety of user mobility and session patterns. In this work, we study the stationary distribution of users by modeling the system as a multi-route queueing network with Poisson inputs. We consider arbitrary routing and arbitrary joint probability distributions for the channel holding times in each route. Through a decomposition-composition approach, we derive a closed-form expression for the joint stationary distribution for the number of users in all cells. The stationary user distribution (1) is insensitive to the user movement patterns, (2) is insensitive to general and dependently distributed channel holding times, (3) depends only on the average arrival rate and average channel holding time at each cell, and (4) is completely characterized by an open network with M/M/∞ queues. We use the Dartmouth trace to validate our analysis, which shows that the analytical model is accurate when new session arrivals are Poisson and remains useful when non-Poisson session arrivals are also included in the data set. Our results suggest that accurate calculation of the user distribution, and other associated metrics such as the system workload, can be achieved with much lower complexity than previously expected.

A Simulation Model of Object Movement for Evaluating the Communication Load in Networked Virtual Environments

In this paper, we propose a common simulation model that can be reused for different performance evaluations of networked virtual environments. To this end, we analyzed the common features of NVEs, in which multiple regions compose a shared space, and where a user has his/her own interest area. Communication architecture can be client-server or peer-server models. In usual simulations, users move around the world while the number of users varies with the system. Our model provides various simulation parameters to customize the region configuration and user movement pattern. Furthermore, our model introduces a way to mimic a lot of users in a minimal experiment environment. The proposed model is integrated with our network framework, which supports various scalability approaches. We specifically applied our model to the interest management and load distribution schemes to evaluate communication overhead. With the proposed simulation model, a new simulation can be easily designed in a large-scale environment.

Movement Pattern Recognition Assisted Map Matching for Pedestrian/Wheelchair Navigation

Today's mobile technology features several sensors that when integrated can provide ubiquitous navigation assistance to pedestrians including wheelchair users. Common sensors found in most smartphones are Global Positioning System (GPS), accelerometer, and compass. In this paper, a user's movement pattern recognition algorithm to improve map matching efficiency and accuracy in pedestrian/wheelchair navigation systems/services is discussed. The algorithm integrates GPS positions, orientation data from compass, and movement states recognized from accelerometer data in a client/server architecture. The algorithm is tested in an Android mobile phone, and the results show that the proposed map matching algorithm is efficient and provides good accuracy.

Two-terminal reliability of a mobile ad hoc network under the asymptotic spatial distribution of the random waypoint model

The random waypoint (RWP) mobility model is frequently used in describing the movement pattern of mobile users in a mobile ad hoc network (MANET). As the asymptotic spatial distribution of nodes under a RWP model exhibits central tendency, the two-terminal reliability of the MANET is investigated as a function of the source node location. In particular, analytical expressions for one and two hop connectivities are developed as well as an efficient simulation methodology for two-terminal reliability. A study is then performed to assess the effect of nodal density and network topology on network reliability.