Review and Analysis on Trajectory Big Data

Recently, the amount of trajectory data has been rapidly increasing with the popularity of LBS and the development of mobile technology. Thus, the analysis of trajectory patterns for large amounts of trajectory data has attracted much interest. To improve the quality of trajectory-based services, it is essential to predict an exact travel time for a given query on road networks. One of the typical schemes for travel time prediction is a rule-based classification method which can ensure high accuracy. However, the existing scheme is inadequate for the processing of massive data because it is designed without the consideration of distributed computing environments. To solve this problem, this paper proposes a travel time prediction algorithm using rule-based classification on MapReduce for a large amount of trajectory data. First, our algorithm generates classification rules based on the actual traffic statistics and measures adequate velocity classes for each road segment. Second, our algorithm generates a distributed index by using the grid-based map partitioning method. Our algorithm can reduces the query processing cost because it only retrieves the grid cells which contain a query region, instead of the entire road network. Furthermore, it can reduce the query processing time by estimating the travel time for each segment of a given query in a parallel way. Finally, we show from our performance analysis that our scheme performs more accurate travel time prediction than the existing algorithms.

Trajectory point classification can be described as a supervised sequence labeling problem, in which a model is trained by labeling data to predict the category of unknown points and identify key events in the trajectory. Due to the difficulty of labeling trajectory point, a large amount of trajectory data is either unlabeled or labeled in an imbalanced way. To make matters worse, traditional trajectory point classification methods are generally constrained to utilize the statistical features of the labeled data and the semantic features as well as the large amount of unlabeled data have not been well studied yet. For this reason, the performance of traditional trajectory point classification methods is far from satisfactory. To solve this problem, we transfer existing language model knowledge to construct the semantic features and construct a trajectory point classification model by combining both the motion features and semantic features. The simulation results show that, compared with the traditional methods, our method has improved the accuracy of trajectory point classification by three and seven percentage points in the classification of circular and turning movements respectively.KeywordsTrajectory point classificationSequence labelingMotion features and semantic featuresLanguage model

The increased use of location-aware devices, such as smartphones, generates a large amount of trajectory data. These data can be useful in several domains, like marketing, path modeling, localization of an epidemic focus, and so on. Nevertheless, since trajectory information contains personal mobility data, improper use or publication of trajectory data can threaten users’ privacy. It may reveal sensitive details like habits of behavior, religious beliefs, and sexual preferences. Therefore, many users might be unwilling to share their trajectory data without a previous anonymization process. Currently, several proposals to address this problem can be found in the literature. These solutions focus on anonymizing data before its publication, i.e., when they are already stored in the server database. Nevertheless, we argue that this approach gives the user no control about the information she shares. For this reason, we propose anonymizing data in the users’ mobile devices, before they are sent to a third party. This article extends our previous work which was, to the best of our knowledge, the first one to anonymize data at the client side, allowing users to select the amount and accuracy of shared data. In this article, we describe an improved version of the protocol, and we include the implementation together with an analysis of the results obtained after the simulation with real trajectory data.

The prevalence of GPS applications and other mobile devices has led to the accumulation of a large amount of trajectory data that contains valuable information for intelligent transportation, route planning, city computing etc. However, massive data not only brings new challenges to data storage and retrieval but also leads to serious privacy risks because of the abundant spatiotemporal information. In this paper, we propose a storage scheme that strikes a balance between the compression ratio and precision. We then introduce a road segment generalization method to address privacy issues stemming from sensitive places. Next, we design a two-layer index mechanism to provide an effective retrieval. Furthermore, a privacy preserving storage system PP-TrajStore is implemented. It provides efficient storage based on a road segment compression scheme, preserves privacy by employing sensitive segment generalization technologies, and achieves rapid retrieval by a two-layer index strategy. Finally, a realworld dataset is utilized to demonstrate the performance of PP-TrajStore

Quantitative examinations of internal representations for arm trajectory planning: minimum commanded torque change model. A number of invariant features of multijoint planar reaching movements have been observed in measured hand trajectories. These features include roughly straight hand paths and bell-shaped speed profiles where the trajectory curvatures between transverse and radial movements have been found to be different. For quantitative and statistical investigations, we obtained a large amount of trajectory data within a wide range of the workspace in the horizontal and sagittal planes (400 trajectories for each subject). A pair of movements within the horizontal and sagittal planes was set to be equivalent in the elbow and shoulder flexion/extension. The trajectory curvatures of the corresponding pair in these planes were almost the same. Moreover, these curvatures can be accurately reproduced with a linear regression from the summation of rotations in the elbow and shoulder joints. This means that trajectory curvatures systematically depend on the movement location and direction represented in the intrinsic body coordinates. We then examined the following four candidates as planning spaces and the four corresponding computational models for trajectory planning. The candidates were as follows: the minimum hand jerk model in an extrinsic-kinematic space, the minimum angle jerk model in an intrinsic-kinematic space, the minimum torque change model in an intrinsic-dynamic-mechanical space, and the minimum commanded torque change model in an intrinsic-dynamic-neural space. The minimum commanded torque change model, which is proposed here as a computable version of the minimum motor command change model, reproduced actual trajectories best for curvature, position, velocity, acceleration, and torque. The model's prediction that the longer the duration of the movement the larger the trajectory curvature was also confirmed. Movements passing through via-points in the horizontal plane were also measured, and they converged to those predicted by the minimum commanded torque change model with training. Our results indicated that the brain may plan, and learn to plan, the optimal trajectory in the intrinsic coordinates considering arm and muscle dynamics and using representations for motor commands controlling muscle tensions.

The precise identification of terminal area arrival procedures is crucial for airspace planning, traffic management, and safety analysis. Traditional methods are limited in automatically detecting specific procedural maneuvers from large amounts of trajectory data. This paper proposes a methodology with knowledge-based filtering to automatically identify three common air traffic control arrival procedures, namely Point Merge System, Vector for Space, and Trombone, from historical trajectory data. After clustering the landing trajectories in the terminal area, we identify the predominant flight patterns. Then, a knowledge-based filtering algorithm, designed based on knowledge of the procedure and geometry criteria, is employed to precisely extract trajectories with different procedure patterns. Experimental results demonstrate that this method effectively identifies the distinct procedural trajectories. An in-depth analysis of the extracted trajectories reveals significant characteristics and differences in their spatial distribution, trajectory structure, and operational efficiency. This work provides data-driven decision support for evaluating terminal area operational performance and arrival procedures.

Location prediction has attracted a significant amount of research effort. Given an object's recent movements and a future time, the goal of location prediction is to predict the location of this object at the future time specified. Prior works have elaborated on mining association relationships among regions, in which objects frequently appear, to predict locations. Association relationships among regions are represented as association rules. By exploring association relationships among regions, prior works are able to have a good accuracy for location prediction. However, with a large amount of trajectory data produced, a huge amount of association rules is expected. Furthermore, trajectory data has both the spatial and temporal information. To further enhance the accuracy of location prediction, one could utilize not only spatial information but also temporal information to estimate locations of objects. In this paper, we propose a spatial-temporal trajectory model (abbreviated as STT) to capture movement behaviors of objects. STT is represented as a probabilistic suffix tree with both spatial and temporal information of movements. Note that STT is able to discover sequential traversal relationships among regions and, for each region, STT derives the corresponding probabilities about the time when objects appear. With the nature of probabilistic suffix tree, we could use a compact structure to capture movement behavior of objects compared to association rules proposed. In light of STT, we further propose an algorithm to traverse STT for location prediction. By exploring both the spatial and temporal information of STT, the accuracy of location prediction is improved. To evaluate our proposed STT and prediction algorithm, we conduct experiments on the synthetic dataset generated from real datasets. Experimental results shows that our proposed STT is able to capture both spatial and temporal patterns of movement behaviors and, by exploring STT, our proposed prediction algorithm outperforms existing works.

The advance of GPS tracking technique brings a large amount of trajectory data. These data can be used in many application domains such as traffic management, urban planning, tourism, and bird migration. Recently, a semantic model which expresses trajectory as a sequence of stops and moves was introduced and become a hot topic for trajectory data analysis. Stops are important parts of trajectories, such as "working at office", "shopping in a mall", "waiting for the bus". Although several works have been developed to discover stops, they considered the characteristics of the stops separately. Because of this limitation, these approaches only focus on certain well-defined trajectories. They cannot work well for heterogeneous cases like diverse and sparse trajectories. Towards stop discovery in trajectories, in this paper, we propose a comprehensive hybrid feature-based method to discover stops. We also evaluate our approach with real-life GPS datasets, and show that this newly proposed approach can provide a good abstraction on the trajectory, with efficient computation.

It is well recognized that human movement in the spatial and temporal dimensions has direct influence on disease transmission(1-3). An infectious disease typically spreads via contact between infected and susceptible individuals in their overlapped activity spaces. Therefore, daily mobility-activity information can be used as an indicator to measure exposures to risk factors of infection. However, a major difficulty and thus the reason for paucity of studies of infectious disease transmission at the micro scale arise from the lack of detailed individual mobility data. Previously in transportation and tourism research detailed space-time activity data often relied on the time-space diary technique, which requires subjects to actively record their activities in time and space. This is highly demanding for the participants and collaboration from the participants greatly affects the quality of data(4). Modern technologies such as GPS and mobile communications have made possible the automatic collection of trajectory data. The data collected, however, is not ideal for modeling human space-time activities, limited by the accuracies of existing devices. There is also no readily available tool for efficient processing of the data for human behavior study. We present here a suite of methods and an integrated ArcGIS desktop-based visual interface for the pre-processing and spatiotemporal analyses of trajectory data. We provide examples of how such processing may be used to model human space-time activities, especially with error-rich pedestrian trajectory data, that could be useful in public health studies such as infectious disease transmission modeling. The procedure presented includes pre-processing, trajectory segmentation, activity space characterization, density estimation and visualization, and a few other exploratory analysis methods. Pre-processing is the cleaning of noisy raw trajectory data. We introduce an interactive visual pre-processing interface as well as an automatic module. Trajectory segmentation(5) involves the identification of indoor and outdoor parts from pre-processed space-time tracks. Again, both interactive visual segmentation and automatic segmentation are supported. Segmented space-time tracks are then analyzed to derive characteristics of one's activity space such as activity radius etc. Density estimation and visualization are used to examine large amount of trajectory data to model hot spots and interactions. We demonstrate both density surface mapping(6) and density volume rendering(7). We also include a couple of other exploratory data analyses (EDA) and visualizations tools, such as Google Earth animation support and connection analysis. The suite of analytical as well as visual methods presented in this paper may be applied to any trajectory data for space-time activity studies.

Up to now, a large amount of trajectory data have been collected by trusted servers because of the wide use of location-based services. One can extract useful information via an analysis of trajectory data. However, the privacy of trajectory bodies risks being inadvertently divulged to others. Therefore, the trajectory data should be properly processed for privacy protection before being released to unknown analysts. This paper proposes a privacy protection scheme for publishing the trajectories with personalized privacy requirements based on the translocation of trajectory points. The algorithm not only enables the published trajectory points to meet the personalized privacy requirements regarding desensitization and anonymity but also preserves the positions of all trajectory points. Our algorithm trades the loss in mobility patterns for the advantage in the similarity of trajectory distance. Related experiments on trajectory data sets with personalized privacy requirements have verified the effectiveness and the efficiency of our algorithm.

Understanding the Design of Mobile Social Networking

Location-based Mobile Social Networks (MSNs) are becoming increasingly popular given the success of Online Social Networks (OSNs), such as Facebook and MySpace, and recent availability of open mobile platforms, such as Apple iPhones and Google Android phones. MSNs extend existing OSNs by allowing a

With the development of Internet technology and the increasing popularity of mobile terminals, mobile social network service has become a convenient way to publish information and share resource in our daily life. However, there lacks a good tool to control the granularity of information dissemination in social network. This paper proposes a method for information or resource sharing with precision control, which can measure distance of users in mobile social network and obtain the level of relationship between users to determine how much, and to what precision level, of users' information and resource can be shared. The method can provide a friendly way to protect privacy and decrease the network burden in mobile social network. We take the positioning information service as an example to illustrate the feasibility of the method. Experiment results have shown that this proposed method can effectively reduce the amount of published information.

A large amount of data about ending users are generated in the interaction over mobile applications, which becomes a valuable data source for sensing human behaviors and public sentiment trends on some topics. Existing works concentrate on traditional feedback data from web sites, which usually come from desktops instead of from mobile terminals. Few studies have been conducted on interactive data from mobile applications such as news aggregation and recommendation applications. In this paper, we propose a system that can model feedback behaviors of mobile users, and can analyze sentiment trends in mobile feedbacks. The testing data are authentic and are dumped from the most frequently used mobile application in China called Toutiao. We propose several analysis methods on sentiment of comments, and modeling algorithms on feedback behaviors. We build a system called MoSa and by using the system, we discover several implicit behavior models and hidden sentiment trends as follows: During news spreading stage, the number of comments grow linearly per month with slope of 3 in 3 months; The dynamics of replying comments are positively correlated with personal daily routines in 24 h; Replying comment behaviors are much more rare than clicking agreement behaviors in mobile applications; The standard deviation of sentiment values in comments are highly influenced by timing stages. Our system and modeling methods provide empirical results for guiding interaction design in mobile Internet, social networks, and blockchain-based crowdsourcing.

In pervasive computing environment, due to the mobility feature of mobile terminals, the mobile service needs to dynamically adapt execution behavior to the changing computing environment as mobile user moves. However, previous researches mainly focused on deploying a service adaption module on mobile terminals or local central server to support the adaptive execution of mobile services, which brings huge overhead to mobile terminals or can hardly meet user's personalized requirements. Therefore, we propose a cloud based framework, called CAMSPF, which includes three parts: RMC (resource management cloud), AMSPC (adaptive mobile service provision cloud), and MSM (mobile service middleware). The CAMSPF deploys the service resources in RMC for realizing efficient resource management and provision, and constructs a PMSAA (private mobile service adaption agent) for each mobile user in AMSPC in order to efficiently support personalized adaptive execution of mobile service. In addition, the MCM is a lightweight software installed on mobile terminals by which CAMSPF can collect user's realtime context and monitor service request from mobile user. Our prototype implementation of CAMSPF verifies that the adaptive execution of mobile services can be performed more efficiently than other traditional approaches, with lower energy consumption on mobile terminals.