A Privacy-Preserving Compression Storage Method for Large Trajectory Data in Road Network

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.

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.

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.

A trajectory can be seen as the imprint left in space by a moving object over time. In recent years, with the widespread use of civil GPS and other positioning devices on mobile terminals and the development and popularity of location-based services and mobile social networks, a large amount of trajectory data is increasingly accumulated in daily life and served by different types of applications. A large amount of trajectory data is generated in the fields of traffic, weather, ecology and mobile services. The effective understanding and utilization of these data requires not only automatic and efficient analysis methods, but also intuitive and vivid visualization, and this paper summarizes the application of trajectory data in cities.

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.

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.

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

Trajectory prediction plays an important role in many urban and marine transportation applications, such as path planning, logistics and traffic management. The existing prediction methods of moving objects mainly focus on trajectory mining in Euclidean space. However, moving objects generally move under road network constraints in the real world. It provides an opportunity to take use of road network constraints or de-facto regular paths for trajectory prediction. As yet, there is little research work on trajectory prediction under road network constraints. And these existing work assumes prior road network information is given in advance. However in some application scenarios, it is very difficult to get road network information, for example the maritime traffic scenario on the wide open ocean. To this end, we propose an approach to trajectory prediction that can make good use of road network constrains without depending on prior road network information. More specifically, our approach extracts road segment polygons from large scale crowdsourcing trajectory data (e.g. AIS positions of ships, GPS positions of vehicles etc.) and translates trajectories into road segment sequences. Useful features such as movement direction and vehicle type are extracted. After that, a LSTM neural network is used to infer the next road segment of a moving object. Experiments on real-world AIS datasets confirm that our approach outperforms the state-of-the-art methods.

Many applications, e.g., Uber, collect large-scale trajectory data from moving vehicles on road network. Trajectory data analytics can benefit many real-world applications, such as route planning and transportation optimizations. Two core operations in trajectory data analytics are trajectory similarity search and join, and both of them rely on a trajectory similarity function to measure the similarity between two trajectories. However, existing similarity functions focus on trajectory points distance and neglect the fact the trajectories should be on road network. Obviously aligning trajectories on road network can remove the noise points introduced by system errors. Toward this goal, we define a road-network-aware trajectory similarity function to measure trajectory similarity. To support trajectory similarity search and join, we propose a filtering-refine framework. In the filtering step, we compute a signature of each trajectory such that if two trajectories are similar, they must share a common signature. We utilize the signatures to prune a huge number of dissimilar pairs. In the refine step, we design effective algorithms to verify the candidates that are not pruned in the filtering step. To support large-scale trajectories, we develop a system DISON for Distributed In-Memory Trajectory Similarity Search and Join on Road Network. DISON splits trajectories into disjoint partitions by considering load balance and locality, and designs effective global index to prune irrelevant partitions. Extensive experiments on real datasets showed that our method achieved high effectiveness, efficiency, and scalability and outperformed existing solutions significantly.

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 pervasiveness of GPS-enabled devices and wireless communication technologies results in massive trajectory data, incurring expensive cost for storage, transmission, and query processing. To relieve this problem, in this paper we propose a novel framework for compressing trajectory data, REST ( Re ference-based S patio- t emporal trajectory compression), by which a raw trajectory is represented by concatenation of a series of historical (sub-)trajectories (called reference trajectories) that form the compressed trajectory within a given spatio-temporal deviation threshold. In order to construct a reference trajectory set that can most benefit the subsequent compression, we propose three kinds of techniques to select reference trajectories wisely from a large dataset such that the resulting reference set is more compact yet covering most footprints of trajectories in the area of interest. To address the computational issue caused by the large number of combinations of reference trajectories that may exist for resembling a given trajectory, we propose efficient greedy algorithms that run in the blink of an eye and dynamic programming algorithms that can achieve the optimal compression ratio. Compared to existing work on trajectory compression, our framework has few assumptions about data such as moving within a road network or moving with constant direction and speed, and better compression performance with fairly small spatio-temporal loss. In addition, by indexing the reference trajectories directly with an in-memory R-tree and building connections to the raw trajectories with inverted index, we develop an extremely efficient algorithm that can answer spatio-temporal range queries over trajectories in their compressed form. Extensive experiments on a real taxi trajectory dataset demonstrate the superiority of our framework over existing representative approaches in terms of both compression ratio and efficiency.

Road intersection is an important part of the road network, and it plays a key role in many intelligent transportation applications. Currently, the generation of road maps required expensive field surveying and labor-intensive mapping. With more and more public vehicles equipped with positioning devices, massive trajectory data are available. These trajectory data provides a new way to generate and update urban road networks in real time. Although numerous methods are currently available to reconstruct road maps from GPS trajectories, most of these methods focus on the generation of road segments (i.e., road centerlines or road lanes). Road intersections are simply represented by nodes, the generation of road intersections with detailed structures still remains a challenge. Therefore, this paper proposes a novel method for constructing road intersections from vehicle GPS trajectories. First, the boundaries of road intersections are detected by turning angles clustering. Then, the entrances and exits of each intersection are identified based on a newly developed method. Finally, the detailed geometric structures of intersections are reconstructed based on the detected entrances and exits, and turning rules are further extracted and assigned to each intersection. The experiments on real-world trajectory data show that the proposed method can generate the detailed geometric structures of road intersections as well as the turning rule semantic information successfully.

On road networks, the clustering of moving objects is important for traffic monitoring and routes recommendation. The existing schemes find out density route by considering the number of vehicles in a road segment. Since they don’t consider the features of each road segment such as width, length, and directions in a road network, the results are not correct in some real road networks. To overcome such problems, we propose a clustering method for congested routes discovering from the trajectories of moving objects on road networks. The proposed scheme can be divided into three steps. First, it divides each road network into segments with different width, length, and directions. Second, the congested road segments are detected through analyzing the trajectories of moving objects on the road network. The saturation degree of each road segment and the average moving speed of vehicles in a road segment are computed to detect the congested road segments. Finally, we compute the final congested routes by using a clustering scheme. The experimental results showed that the proposed scheme can efficiently discover the congested routes in different directions of the roads.