Trajectory Mining Research Articles

An Orientation Method with Prediction and Anticipation Features


 
 
 Nowadays, progress is constant and inherent to a living society. This may occur in different arenas, namely in mathematical evaluation and healthcare. Assistive technologies are a topic under this evolution, being extremely important in helping users with diminished capabilities (physical, sensory, intellectual). These technologies assist people in tasks that were difficult or impossible to execute. A common diminished task is orientation, which is crucial for the user autonomy. The adaptation to such technologies should require the minimum effort possible in order to enable the person to use devices that convey assistive functionalities. There are several solutions that help a human being to travel between two different locations, however their authors are essentially concerned with the guidance method, giving special attention to the user interface. The CogHelper system aims to overcome these systems by applying a framework of Speculative Computation, which adds a prediction feature for the next user movement giving an anticipation ability to the system. Thus, an alert is triggered before the user turn towards an incorrect path. The travelling path is also adjusted to the user preferences through a trajectory mining module.
 
 

Trajectory anonymisation based on graph split using EMD

Analysing and mining trajectories pose new challenges for trajectory privacy. We are addressing privacy issue for offline (historical) trajectories which are generally published for research. A fundamental research question in trajectory privacy domain is of trajectory anonymisation. k-anonymity is used as a standard for privacy which ensures that every entity in the dataset is indistinguishable from (k − 1) other entities. The proposed work aims at anonymising trajectories based on graph split method. We have used technique of constructing trajectory graph to simulate spatial and temporal relations of trajectories, based on which trajectory k-anonymity sets are found through graph split. For the purpose, we have proposed a novel method that uses earth mover's distance as a metric to find trajectory k-anonymity sets in contrast to Euclidean distance. It is discovered through a series of experiments that the proposed method is outperforming in terms of low information loss and computation time.

Trajectory anonymisation based on graph split using EMD

Analysing and mining trajectories pose new challenges for trajectory privacy. We are addressing privacy issue for offline (historical) trajectories which are generally published for research. A fundamental research question in trajectory privacy domain is of trajectory anonymisation. k-anonymity is used as a standard for privacy which ensures that every entity in the dataset is indistinguishable from (k − 1) other entities. The proposed work aims at anonymising trajectories based on graph split method. We have used technique of constructing trajectory graph to simulate spatial and temporal relations of trajectories, based on which trajectory k-anonymity sets are found through graph split. For the purpose, we have proposed a novel method that uses earth mover's distance as a metric to find trajectory k-anonymity sets in contrast to Euclidean distance. It is discovered through a series of experiments that the proposed method is outperforming in terms of low information loss and computation time.

Trajectory data mining: A review of methods and applications

The increasing use of location-aware devices has led to an increasing availability of trajectory data. As a result, researchers devoted their efforts to developing analysis methods including different data mining methods for trajectories. However, the research in this direction has so far produced mostly isolated studies and we still lack an integrated view of problems in applications of trajectory mining that were solved, the methods used to solve them, and applications using the obtained solutions. In this paper, we first discuss generic methods of trajectory mining and the relationships between them. Then, we discuss and classify application problems that were solved using trajectory data and relate them to the generic mining methods that were used and real world applications based on them. We classify trajectory-mining application problems under major problem groups based on how they are related. This classification of problems can guide researchers in identifying new application problems. The relationships between the methods together with the association between the application problems and mining methods can help researchers in identifying gaps between methods and inspire them to develop new methods. This paper can also guide analysts in choosing a suitable method for a specific problem. The main contribution of this paper is to provide an integrated view relating applications of mining trajectory data and the methods used.

Forecasting Public Transit Use by Crowdsensing and Semantic Trajectory Mining: Case Studies

With the growing development of smart cities, public transit forecasting has begun to attract significant attention. In this paper, we propose an approach for forecasting passenger boarding choices and public transit passenger flow. Our prediction model is based on mining common user behaviors for semantic trajectories and enriching features using knowledge from geographic and weather data. All the experimental data comes from the Ridge Nantong Limited bus company and Alibaba platform which is also open to the public. We evaluate our approach using various data sources, including point of interest (POI), weather condition, and public bus information in Guangzhou to demonstrate its effectiveness. Experimental results show that our proposal performs better than baselines in the prediction of passenger boarding choices and public transit passenger flow.

Precision Advertising Based on the Scene of Trajectory Mining

Precision Advertising Based on the Scene of Trajectory Mining

Multidimensional temporal mining in hospital information system

Since medical and healthcare data include temporal trends of clinical symptoms, temporal data mining is one of the most important elements to discover knowledge. In this paper, we propose a three-dimensional trajectories mining method to capture the similarities between temporal trajectories of three selected variables. The method was evaluated on two datasets: one is on chronic hepatitis, and the other is on temporal trends of # orders in our university hospital. The results showed that, compared with conventional studies, the method gave more detailed classification of temporal trends.

Identifying K Primary Corridors from urban bicycle GPS trajectories on a road network

Given a set of GPS tracks on a road network and a number k, the K-Primary-Corridor (KPC) problem aims to identify k tracks as primary corridors such that the overall distance from all tracks to their closest primary corridors is minimized. The KPC problem is important to domains such as transportation services interested in finding primary corridors for public transportation or greener travel (e.g., bicycling) by leveraging emerging GPS trajectory datasets. However, the problem is challenging due to the large amount of shortest path distance computations across tracks. Related trajectory mining approaches, e.g., density or frequency based hot-routes, focus on anomaly detection rather than identifying representative corridors minimizing total distances from other tracks, and thus may not be effective for the KPC problem. Our recent work proposed a k-Primary Corridor algorithm that precomputes a column-wise lookup table of network Hausdorff distances. This paper extends our recent work with a new computational algorithm based on lower bound filtering. We design lower bounds of network Hausdorff distances based on the concept of track envelopes and propose three different track envelope formation strategies based on random selection, overlap, and Jaccard coefficient respectively. Theoretical analysis on proof of correctness as well as computational cost models are provided. Extensive experiments and case studies show that our new algorithm with lower bound filtering significantly reduces the computational time of our previous algorithm, and can help effectively determine primary bicycle corridors.

Modeling, Mining, and Analyzing Semantic Trajectories: The Process to Extract Meaningful Behaviors of Moving Objects

devices leave a huge number of digital traces that are collected as describing the movement of its users or a path followed by any moving object in geographical space over some period of time. However, those mobile devices provide just raw trajectories (x, y, t), ignoring information about their related contextual data, these additional data contribute in producing significant knowledge about movements and provide applications with richer and more meaningful knowledge. Therefore, researchers focus on transforming raw trajectories into trajectories by combining the raw mobility tracks with related contextual data and creating a new type of trajectories called semantic trajectories, then applying mining techniques. This paper study closely the current researches on modeling and mining trajectories so far, and try to investigate by proposing a descriptive schema including all steps that users can browse from the construction of the trajectories to the analyze of behaviors extracted. Keywordsemantic extracting knowledge, enrichment, spatial data mining.

A Unifying Framework of Mining Trajectory Patterns of Various Temporal Tightness

Discovering trajectory patterns is shown to be very useful in learning interactions between moving objects. Many types of trajectory patterns have been proposed in the literature, but previous methods were developed for only a specific type of trajectory patterns. This limitation could make pattern discovery tedious and inefficient since users typically do not know which types of trajectory patterns are hidden in their data sets. Our main observation is that many trajectory patterns can be arranged according to the strength of temporal constraints. In this paper, we propose a unifying framework of mining trajectory patterns of various temporal tightness, which we call unifying trajectory patterns ( UT-patterns ). This framework consists of two phases: initial pattern discovery and granularity adjustment . A set of initial patterns are discovered in the first phase, and their granularities (i.e., levels of detail) are adjusted by split and merge to detect other types in the second phase. As a result, the structure called a pattern forest is constructed to show various patterns. Both phases are guided by an information-theoretic formula without user intervention. Experimental results demonstrate that our framework facilitates easy discovery of various patterns from real-world trajectory data.

Trajectory Data Mining

The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles, and animals. Many techniques have been proposed for processing, managing, and mining trajectory data in the past decade, fostering a broad range of applications. In this article, we conduct a systematic survey on the major research into trajectory data mining , providing a panorama of the field as well as the scope of its research topics. Following a road map from the derivation of trajectory data, to trajectory data preprocessing, to trajectory data management, and to a variety of mining tasks (such as trajectory pattern mining, outlier detection, and trajectory classification), the survey explores the connections, correlations, and differences among these existing techniques. This survey also introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors, to which more data mining and machine learning techniques can be applied. Finally, some public trajectory datasets are presented. This survey can help shape the field of trajectory data mining , providing a quick understanding of this field to the community.

A Trajectory Data Clustering Method Based On Dynamic Grid Density

Under the traditional method of frequent trajectory mining, the location of data is obtained through the GPS device. However, limited equipment accuracy may incur location ambiguity. In this paper, we propose a new trajectory data clustering method based on dynamic grid density, in order to remove this ambiguity. In this method, the trajectory space of an object is firstly divided into equal-sized squares dynamically. Then the trajectories of object are mapped to their corresponding square. Next, the density of each grid is calculated and all the frequent squares are acquired given the minimum support threshold. Lastly, the frequent area is obtained by merging the frequent squares acquired previously, using the boundary function provided. The experimental results show that this method provides an optional way of finding the frequent movement sequence.

Spatial Uncertainty Trajectory Dataset Mining Based on Two-stages Dynamic Division

Due to the measurement precision, transmission delay and so on, we could only obtain uncertainty position information of moving objects. Spatial uncertainty trajectory data is uncertain in location of mobile objects. It is leading to the challenge of modeling uncertainty trajectory data and mining usable knowledge about movement pattern. In this paper, we propose a two-stages dynamic division method to dealing with the spatial uncertainty trajectory. The approach presents the notions of adjacent boundary cells and shared cells, and merges these cells into basic cells through distance and density membership degree. A comprehensive performance study on synthetic datasets shows that the proposed method in both effectiveness and scalability.

A Framework of Mining Trajectories from Untrustworthy Data in Cyber-Physical System

A cyber-physical system (CPS) integrates physical (i.e., sensor) devices with cyber (i.e., informational) components to form a context-sensitive system that responds intelligently to dynamic changes in real-world situations. The CPS has wide applications in scenarios such as environment monitoring, battlefield surveillance, and traffic control. One key research problem of CPS is called mining lines in the sand . With a large number of sensors (sand) deployed in a designated area, the CPS is required to discover all trajectories (lines) of passing intruders in real time. There are two crucial challenges that need to be addressed: (1) the collected sensor data are not trustworthy, and (2) the intruders do not send out any identification information. The system needs to distinguish multiple intruders and track their movements. This study proposes a method called LiSM (Line-in-the-Sand Miner) to discover trajectories from untrustworthy sensor data. LiSM constructs a watching network from sensor data and computes the locations of intruder appearances based on the link information of the network. The system retrieves a cone model from the historical trajectories to track multiple intruders. Finally, the system validates the mining results and updates sensors’ reliability scores in a feedback process. In addition, LoRM (Line-on-the-Road Miner) is proposed for trajectory discovery on road networks— mining lines on the roads . LoRM employs a filtering-and-refinement framework to reduce the distance computational overhead on road networks and uses a shortest-path-measure to track intruders. The proposed methods are evaluated with extensive experiments on big datasets. The experimental results show that the proposed methods achieve higher accuracy and efficiency in trajectory mining tasks.

Road-Network Aware Trajectory Clustering: Integrating Locality, Flow, and Density

Mining trajectory data has been gaining significant interest in recent years. However, existing approaches to trajectory clustering are mainly based on density and Euclidean distance measures. We argue that when the utility of spatial clustering of mobile object trajectories is targeted at road-network aware location-based applications, density and Euclidean distance are no longer the effective measures. This is because traffic flows in a road network and the flow-based density characterization become important factors for finding interesting trajectory clusters. We propose NEAT-a road-network aware approach for fast and effective clustering of trajectories of mobile objects traveling in road networks. Our approach carefully considers the traffic locality characterized by the physical constraints of the road network, the traffic flow among consecutive road segments, and the flow-based density to organize trajectories into spatial clusters in a comprehensive three-phase clustering framework. NEAT discovers spatial clusters as groups of sub-trajectories which describe both dense and highly continuous flows of mobile objects. We perform extensive experiments with mobility traces generated using different scales of real road networks. Experimental results demonstrate the flexibility of the NEAT system and show that NEAT is highly accurate and runs orders of magnitude faster than existing density-based trajectory clustering approaches.

Mining Users' Similarity from Moving Trajectories for Mobile E-commerce Recommendation

Users’ similarity mining in mobile e-commerce systems is an important field with wide applications, such as personalized recommendation and accurate advertising. Moving trajectories of e-commerce users contain much useful information, providing a very good opportunity for understanding the users’ interesting and discovering the similarity between mobile-device-holders. In this paper, we explores the problems in the existing mobile ecommerce recommendation methods, and propose a mobile users’ moving trajectories mining based user similarity discovering approach for mobile e-commerce system. We formally defines the moving trajectory and view the areas, where users stay within for a certain time, as interested regions, which reflect the preferences of mobile-device-holders. Based on the number of overlapped interested areas, a user similarity measure method is proposed. Experimental evaluation, conducted based on the publicly available datasets commendably demonstrate the effectiveness of our approach.

Nonlinear Machine Learning of Patchy Colloid Self-Assembly Pathways and Mechanisms

Bottom-up self-assembly offers a means to synthesize materials with desirable structural and functional properties that cannot easily be fabricated by other techniques. An improved understanding of the structural pathways and mechanisms by which self-assembling materials spontaneously form from their constituent building blocks is of value in understanding the fundamental principles of assembly and in guiding inverse building block design. We present an approach to infer systematically assembly pathways and mechanisms by nonlinear data mining of molecular simulation trajectories using diffusion maps. We have validated our methodology in applications to Brownian dynamics simulations of the assembly of anisotropic "patchy colloids" into polyhedral aggregates. For particles designed to form tetrahedral aggregates, we identify two divergent assembly pathways leading to chains of interlocking dimers and tetramers and chains of interlocking trigonal planar trimers. For particles designed to assemble icosahedral aggregates, our approach recovers two distinct assembly pathways corresponding to monomeric addition and budding from a disordered liquid phase. These assembly routes were previously reported by inspection of simulation trajectories by Wilber et al. ( J. Chem. Phys. 2007, 127, 085106 ; J. Chem. Phys. 2009, 131, 175102 ), validating the capacity of our approach to systematically recover assembly mechanisms previously discernible only by trajectory visualization.

Sports Play Visualization System Using Trajectory Mining Method

Abstract In this paper, a sports strategy decision support system, called SportsViz, for ball games is presented. Both the results of statistical analysis and player motions are visualized in an integrated way to facilitate easy decision making by this system. The player motions can be easily aggregated with our newly developed trajectory mining technique for deep consideration on the motions. This paper also describes how the system can be used for effective strategy analysis in American football.

Mining geographic-temporal-semantic patterns in trajectories for location prediction

In recent years, research on location predictions by mining trajectories of users has attracted a lot of attention. Existing studies on this topic mostly treat such predictions as just a type of location recommendation, that is, they predict the next location of a user using location recommenders. However, an user usually visits somewhere for reasons other than interestingness. In this article, we propose a novel mining-based location prediction approach called Geographic-Temporal-Semantic-based Location Prediction (GTS-LP), which takes into account a user's geographic-triggered intentions, temporal-triggered intentions, and semantic-triggered intentions, to estimate the probability of the user in visiting a location. The core idea underlying our proposal is the discovery of trajectory patterns of users, namely GTS patterns , to capture frequent movements triggered by the three kinds of intentions. To achieve this goal, we define a new trajectory pattern to capture the key properties of the behaviors that are motivated by the three kinds of intentions from trajectories of users. In our GTS-LP approach, we propose a series of novel matching strategies to calculate the similarity between the current movement of a user and discovered GTS patterns based on various moving intentions. On the basis of similitude, we make an online prediction as to the location the user intends to visit. To the best of our knowledge, this is the first work on location prediction based on trajectory pattern mining that explores the geographic, temporal, and semantic properties simultaneously. By means of a comprehensive evaluation using various real trajectory datasets, we show that our proposed GTS-LP approach delivers excellent performance and significantly outperforms existing state-of-the-art location prediction methods.

Trajectory data mining: integrating semantics

Purpose – Trajectory is the path a moving object takes in space. To understand the trajectory movement patters, data mining is used. However, pattern analysis needs semantics to be understood. Therefore, the purpose of this paper is to enrich trajectories with semantic annotations, such as the name of the location where the trajectory has stopped, so that the paper is able to attain quality decisions. Design/methodology/approach – An experiment was conducted to explain that the use of raw trajectories alone is not enough for the decision-making process and detailed pattern extraction. Findings – The findings of the paper indicates that some fundamental patterns and knowledge discovery is only obtainable by understanding the semantics underlying the position of each point. Research limitations/implications – The unavailability of data are a limitation of the paper, which would limit its generalizability. Additionally, the lack of availability of tools for automatically adding semantics to clusters posed as a limitation of the paper. Practical implications – The paper encourages governments as well as businesses to analyze movement data using data mining techniques, in light of the surrounding semantics. This will allow, for example, solving traffic congestions, since by understanding the movement patterns, the traffic authority could make decisions in order to avoid such congestions. Moreover, it could also help tourism authorities, at national levels, to know tourist movement patterns and support these patterns with the required logistical support. Additionally, for businesses, mobile operators could dynamically enhance their services, voice and data, by knowing the semantically enriched patterns of movement. Originality/value – The paper contributes to the already rare literature on trajectory mining, enhanced with semantics. Mainstream literature focusses on either trajectory mining or semantics, therefore the paper claims that the approach is novel and is needed as well. By integrating mining outcomes with semantic annotation, the paper contributes to the body of knowledge and introduces, with lab evidence, the new approach.