Analysis Of Trajectory Data Research Articles

Real‐time detection of traffic congestion based on trajectory data

Traffic congestion is common in most cities. It not only affects people's normal travel time but also causes more traffic crashes. To solve the traffic congestion and reduce the corresponding hazards, it is necessary to quickly detect the location of traffic congestion. In view of the fact that the trajectory data record the temporal and spatial information of moving objects, this article presents two methods for the real-time detection of traffic congestion through real-time processing of trajectory data. One is to use distributed densit-based spatial clustering of applications with noise (DBSCAN) clustering to detect the location of traffic congestion. The other is to perform distributed topology analysis of trajectory data to find congested areas. Finally, extensive experiments that involved using three real datasets to simulate both real-time detection methods on Spark Streaming demonstrated the efficiency of the two methods.

A User Study on the Capability of Three Geo-Based Features in Analyzing and Locating Trajectories

Visual analysis is widely applied to study human mobility due to the ability of integrating contextual information from multiple data sources. Analyzing trajectory data through visualization improves the efficiency and accuracy of the analysis, yet it may induce exposure of the location privacy. To balance the location privacy and analysis effectiveness, this work focuses on the behaviors of different geo-based contexts in the process of trajectory interpretation. Three types of geo-based contexts are identified after surveying 94 related literatures. We further conduct experiments to investigate their capability by evaluating how they benefit the analysis, and whether they lead to location privacy exposure. Finally, we report and discuss interesting findings, and provide guidelines to the design of privacy-preserving analysis approaches for human periodic trajectories.

Analytical queries on semantic trajectories using graph databases

AbstractThis article studies the analysis of moving object data collected by location‐aware devices, such as GPS, using graph databases. Such raw trajectories can be transformed into so‐called semantic trajectories, which are sequences of stops that occur at “places of interest.” Trajectory data analysis can be enriched if spatial and non‐spatial contextual data associated with the moving objects are taken into account, and aggregation of trajectory data can reveal hidden patterns within such data. When trajectory data are stored in relational databases, there is an “impedance mismatch” between the representation and storage models. Graphs in which the nodes and edges are annotated with properties are gaining increasing interest to model a variety of networks. Therefore, this article proposes the use of graph databases (Neo4j in this case) to represent and store trajectory data, which can thus be analyzed at different aggregation levels using graph query languages (Cypher, for Neo4j). Through a real‐world public data case study, the article shows that trajectory queries are expressed more naturally on the graph‐based representation than over the relational alternative, and perform better in many typical cases.

STEFTR: A Hybrid Versatile Method for State Estimation and Feature Extraction From the Trajectory of Animal Behavior.

Animal behavior is the final and integrated output of brain activity. Thus, recording and analyzing behavior is critical to understand the underlying brain function. While recording animal behavior has become easier than ever with the development of compact and inexpensive devices, detailed behavioral data analysis requires sufficient prior knowledge and/or high content data such as video images of animal postures, which makes it difficult for most of the animal behavioral data to be efficiently analyzed. Here, we report a versatile method using a hybrid supervised/unsupervised machine learning approach for behavioral state estimation and feature extraction (STEFTR) only from low-content animal trajectory data. To demonstrate the effectiveness of the proposed method, we analyzed trajectory data of worms, fruit flies, rats, and bats in the laboratories, and penguins and flying seabirds in the wild, which were recorded with various methods and span a wide range of spatiotemporal scales—from mm to 1,000 km in space and from sub-seconds to days in time. We successfully estimated several states during behavior and comprehensively extracted characteristic features from a behavioral state and/or a specific experimental condition. Physiological and genetic experiments in worms revealed that the extracted behavioral features reflected specific neural or gene activities. Thus, our method provides a versatile and unbiased way to extract behavioral features from simple trajectory data to understand brain function.

JumpDetector: An automated monitoring equipment for the locomotion of jumping insects.

Continuous jumping behavior, a kind of endurance locomotion, plays important roles in insect ecological adaption and survival. However, the methods used for the efficient evaluation of insect jumping behavior remain largely lacking. Here, we developed a locomotion detection system named JumpDetector with automatic trajectory tracking and data analysis to evaluate the jumping of insects. This automated system exhibits more accurate, efficient, and adjustable performance than manual methods. By using this automatic system, we characterized a gradually declining pattern of continuous jumping behavior in 4th‐instar nymphs of the migratory locust. We found that locusts in their gregarious phase outperformed locusts in their solitary phase in the endurance jumping locomotion. Therefore, the JumpDetector could be widely used in jumping behavior and endurance locomotion measurement.

Characterizing Lane Changes via Digitized Infrastructure and Low-Cost GPS

With the expected increase in the availability of trajectory-level information from connected and autonomous vehicles, issues of lane changing behavior that were difficult to assess with traditional freeway detection systems can now begin to be addressed. This study presents the development and application of a lane change detection algorithm that uses trajectory data from a low-cost GPS-equipped fleet, supplemented with digitized lane markings. The proposed algorithm minimizes the effect of GPS errors by constraining the temporal duration and lateral displacement of a lane change detected using preliminary lane positioning. The algorithm was applied to 637 naturalistic trajectories traversing a long weaving segment and validated through a series of controlled lane change experiments. Analysis of naturalistic trajectory data revealed that ramp-to-freeway trips had the highest number of discretionary lane changes in excess of 1 lane change/vehicle. Overall, excessive lane change rates were highest between the two middle freeway lanes at 0.86 lane changes/vehicle. These results indicate that extreme lane changing behavior may significantly contribute to the peak-hour congestion at the site. The average lateral speed during lane change was 2.7 fps, consistent with the literature, with several freeway–freeway and ramp–ramp trajectories showing speeds up to 7.7 fps. All ramp-to-freeway vehicles executed their first mandatory lane change within 62.5% of the total weaving length, although other weaving lane changes were spread over the entire segment. These findings can be useful for implementing strategies to lessen abrupt and excessive lane changes through better lane pre-positioning.

Using big GPS trajectory data analytics for vehicle miles traveled estimation

As location-sensing devices and apps become more prevalent, the scale and availability of big GPS trajectory data are also rapidly expanding. Big GPS trajectory data analytics offers new opportunities for gaining insights into vehicle movement dynamics and road network usage patterns that are important for transportation studies and urban planning among other fields. Processing big GPS trajectory data, consisting of billions of GPS waypoints and millions of individual trajectories is a challenging yet important task for researchers from these different domains. In this research, we propose an Apache Spark-based geo-computing framework for using big GPS trajectory data to estimate vehicle miles travelled, an important metric used by both federal and state highway agencies in the United States for transportation planning. The computing challenge lies in scaling the processing of billions of raw GPS points data as well as the steps for map matching for a statewide road network consisting of thousands of road segments. In this work, we develop a scalable map-matching module that considers both the spatiotemporal information of GPS waypoint sequences and topologic information of road network for the State of Maryland while striking a balance between matching accuracy and computing time. We processed 19.8 million raw GPS trips consisting of approximately 1.4 billion GPS waypoints collected in Maryland during a four-month period in 2015 to estimate vehicle miles travelled for Maryland’s road network. The estimation results show that using big GPS trajectory analytic methods is promising for obtaining accurate and stable vehicle miles travelled estimates.

An effective and versatile distance measure for spatiotemporal trajectories

The analysis of large-scale trajectory data has tremendous benefits for applications ranging from transportation planning to traffic management. A fundamental building block for the analysis of such data is the computation of similarity between trajectories. Existing work for similarity computation focuses mainly on the spatial aspects of trajectories, but more rarely takes into account time in conjunction with space. A key challenge when considering time is how to handle trajectories that are sampled asynchronously or at variable rates, which can lead to uncertainty. To tackle this problem, we quantify trajectory similarity as an interval, rather than a single value, to capture the uncertainty that can result from different sampling rates and asynchronous sampling. Based on this perspective, we develop a new trajectory similarity measure, Trajectory Interval Distance Estimation, which models similarity computation as a convex optimisation problem. Using two real datasets, we demonstrate that our proposed measure is extremely effective for assessing similarity in comparison to existing state of the art measures.

Efficient learning algorithm for sparse subsequence pattern-based classification and applications to comparative animal trajectory data analysis

ABSTRACTRecent advances in robotics and measurement technologies have enabled biologists to record the trajectories created by animal movements. In this paper, we convert time series of animal trajectories into sequences of finite symbols, and then propose a machine learning method for gaining biological insight from the trajectory data in the form of symbol sequences. The proposed method is used for training a classifier which differentiates between the trajectories of two groups of animals such as male and female. The classifier is represented in the form of a sparse linear combination of subsequence patterns, and we call the classifier an S3P-classifier. The trained S3P-classifier is easy to interpret because each coefficient represents the specificity of the subsequence patterns in either of the two classes of animal trajectories. However, fitting an S3P-classifier is computationally challenging because the number of subsequence patterns is extremely large. The main technical contribution in this paper is the development of a novel algorithm for overcoming this computational difficulty by combining a sequential mining technique with a recently developed convex optimization technique called safe screening. We demonstrate the effectiveness of the proposed method by applying it to three animal trajectory data analysis tasks.

Interactive visual analysis of spatiotemporal characteristics in tropical cyclone trajectory data

This paper presents an integrated visualization system that enables interactive visual analysis of trajectory data of tropical cyclones. This system couples together a novel visual exploring interface with a progressive-partition based trajectory clustering algorithm to enable users to interactively explore spatiotemporal patterns of tropical cyclone. The position, intensity change, and movement of cyclone can be observed vividly from the 2D spatial view. The occurrence frequencies, the lifetime, seasonality distribution, and other statistical features are computed on the fly during analysis and are inspected in multiple linked views. The experimental results with real cyclone data show the effectiveness of the proposed approach.

Flight trajectory data analytics for characterization of air traffic flows: A comparative analysis of terminal area operations between New York, Hong Kong and Sao Paulo

Future Air Traffic Management systems can benefit from innovative approaches that leverage the increasing availability of operational data to facilitate the development of new performance assessment and decision-support capabilities. This paper presents a data analytics framework for high-fidelity characterization of air traffic flows from large-scale flight tracking data. Machine learning methods are used to exploit spatiotemporal patterns in aircraft movement towards the identification of trajectory patterns and traffic flow patterns. The outcomes and potential impacts of this framework are demonstrated with a comparative analysis of terminal area operations in three representative multi-airport (metroplex) systems of the global air transportation system: New York, Hong Kong and Sao Paulo. As a descriptive tool for systematic analysis of the flow behavior, the framework allows for cross-metroplex comparisons of terminal airspace design, utilization and traffic performance. Novel quantitative metrics are created to summarize metroplex efficiency, capacity and predictability. The results reveal several structural, operational and performance differences between the multi-airport systems analyzed. Our findings show that New York presents the most complex airspace design, with considerably higher number of routes and interactions between them, as well as more dynamic changes in the terminal area flow structure during the day, in part driven by the presence of flow dependencies. Interestingly, it exhibits the best levels of traffic flow efficiency on average, both spatially and temporally, yet the highest variability in metroplex configuration performance, with more pronounced performance degradation during inclement weather.

Visual analysis of traffic data via spatio-temporal graphs and interactive topic modeling

Analyzing of traffic data is an important task for urban planners and managers. Traffic data have the spatio-temporal characteristics, which can reflect the variation of the presence of vehicle in different places over time as well as traffic flow dynamics among different places. The analysis of large-scale GPS trajectory data is a very challenging research due to the complexity of data and the need to extract useful information under cover in data. In this study, we combine temporal and geospatial aggregation of traffic data for obtaining key areas and creating legible traffic flow maps; meanwhile, we make full use of the topic model to capture latent semantic information. Nevertheless, most of the topic models always encounter the plague of choosing the optimal number of topics and cannot easily incorporate numerous types of user feedback. To tackle these problems, we propose an interactive topic modeling equipped with various interactive capabilities which empowers users to explore data from different levels of detail. Finally, we design and implement an interactive visual analytics prototype system based on the spatio-temporal graphs and the interactive topic modeling. The feasibility and validity of our system is demonstrated by conducting two case studies with a real-world traffic data in Hangzhou.

Integrating algebraic multigrid method in spatial aggregation of massive trajectory data

ABSTRACTThe advanced technologies in location-based services and telecom have yield large volumes of trajectory data. Understanding these data effectively requires intuitive yet accurate visual analysis. The visual analysis of massive trajectory data is challenged by the numerous interactions among different locations, which cause massive clutter. This paper presents a new methodology for visual analysis by integrating algebraic multigrid (AMG) method in data aggregation. The non-parametric method helps to build a multi-layer node representation from a graph which is extracted from trajectory data. The comparison with AMG and other methods shows that AMG method is more advanced in both the spatial representation and the importance of nodes. The new method is tested with real-world dataset of cell-phone signalling records in Beijing. The results show that our method is suitable for processing and creating abstraction of massive trajectory dataset, revealing inherent patterns and creating intuitive and vivid flow maps.

HTMoL: full-stack solution for remote access, visualization, and analysis of molecular dynamics trajectory data.

Research on biology has seen significant advances with the use of molecular dynamics (MD) simulations. The MD methodology enables explanation and discovery of molecular mechanisms in a wide range of natural processes and biological systems. The need to readily share the ever-increasing amount of MD data has been hindered by the lack of specialized bioinformatic tools. The difficulty lies in the efficient management of the data, i.e., in sending and processing 3D information for its visualization. In this work, we present HTMoL, a plug-in-free, secure GPU-accelerated web application specifically designed to stream and visualize MD trajectory data on a web browser. Now, individual research labs can publish MD data on the Internet, or use HTMoL to profoundly improve scientific reports by including supplemental MD data in a journal publication. HTMoL can also be used as a visualization interface to access MD trajectories generated on a high-performance computer center directly. Furthermore, the HTMoL architecture can be leveraged with educational efforts to improve learning in the fields of biology, chemistry, and physics.

IBVis: Interactive Visual Analytics for Information Bottleneck Based Trajectory Clustering.

Analyzing trajectory data plays an important role in practical applications, and clustering is one of the most widely used techniques for this task. The clustering approach based on information bottleneck (IB) principle has shown its effectiveness for trajectory data, in which a predefined number of the clusters and an explicit distance measure between trajectories are not required. However, presenting directly the final results of IB clustering gives no clear idea of both trajectory data and clustering process. Visual analytics actually provides a powerful methodology to address this issue. In this paper, we present an interactive visual analytics prototype called IBVis to supply an expressive investigation of IB-based trajectory clustering. IBVis provides various views to graphically present the key components of IB and the current clustering results. Rich user interactions drive different views work together, so as to monitor and steer the clustering procedure and to refine the results. In this way, insights on how to make better use of IB for different featured trajectory data can be gained for users, leading to better analyzing and understanding trajectory data. The applicability of IBVis has been evidenced in usage scenarios. In addition, the conducted user study shows IBVis is well designed and helpful for users.

UlTraMan

Massive trajectory data is being generated by GPS-equipped devices, such as cars and mobile phones, which is used increasingly in transportation, location-based services, and urban computing. As a result, a variety of methods have been proposed for trajectory data management and analytics. However, traditional systems and methods are usually designed for very specific data management or analytics needs, which forces users to stitch together heterogeneous systems to analyze trajectory data in an inefficient manner. Targeting the overall data pipeline of big trajectory data management and analytics, we present a unified platform, termed as UlTraMan. In order to achieve scalability, efficiency, persistence , and flexibility , (i) we extend Apache Spark with respect to both data storage and computing by seamlessly integrating a key-value store, and (ii) we enhance the MapReduce paradigm to allow flexible optimizations based on random data access. We study the resulting system's flexibility using case studies on data retrieval, aggregation analyses, and pattern mining. Extensive experiments on real and synthetic trajectory data are reported to offer insight into the scalability and performance of UlTraMan.

A branch and bound strategy for Fast Trajectory Similarity Measuring

The increasing use of GPS-enabled devices allowed the collection of huge volumes of movement data in the form of trajectories. An important research problem in trajectory data analysis is the similarity measurement. For most applications, a trajectory-to-trajectory comparison is needed, and therefore, scalability of trajectory similarity measures directly impact the viability to use these techniques. Most similarity measures adopt a dynamic programming implementation, which has a quadratic time complexity in all cases, computing the pair-wise distance for all trajectory points, thus limiting the scalability of these measures. In this article we present a new strategy which takes into account the distance properties in Euclidean spaces to reduce the number of pair-wise point comparison required to determine all the matching points of two trajectories. An extensive experimental evaluation over real GPS trajectory datasets demonstrates the pruning power over 85% in the number of distance computations required to determine the matchings, and a significant execution time speed-up of up to one order of magnitude over the dynamic programming approach.

Power estimates of an undulating membrane tidal energy converter

An experimental study of an undulating membrane based on the Eel Energy device is presented in this paper. This system uses interaction between a semi-rigid plate and an axial flow to generate undulation. At full scale, the deformation of the structure is then converted into electrical power using linear electromagnetic generators. In order to simulate the power take-off on a 1/20th scale prototype, hydraulic dampers are located all over the length of the device. The dampers have non-linear behaviour. A representative damping parameter have been introduced to study their impact.Results are presented in function of fluid velocity and damping adjustment. Undulation mode, frequency and forces on the structure are described. Results show that fluid velocity has a destabilizing effect on the membrane, increasing undulation frequency and lowering its amplitude, while damping has the opposite effect. Furthermore, two methods used to evaluate the power conversion are described and evaluated from trajectory and forces data analysis. Power estimation shows an evolution proportional to cubic current speed at low flow speed, as for classical tidal turbines. Power also seems to scale with the square of the product between undulation frequency and amplitude.

Research and Application of GPS Trajectory Data Visualization

Taxi trajectory data is a kind of massive traffic data with spatial–temporal dimensions, and plays a key role in traffic management, travel analysis and route recommendation for residents. Analyzing trajectory data with traditional methods is complicated, but visualization techniques can intuitively reflect the change trend of spatial–temporal data and facilitate the mining of knowledge and laws in the data. A novel taxi trajectory data visualization and analysis system, TaxiVis, has been designed and developed in this study. This system not only displays the traveling routes of every taxi on the map at the micro-level, dynamically analyzing every taxi’s operating indicators with varying time, but also displays the operating statistics of every taxi company at the macro-level. In addition, the TaxiVis provides route inquiry recommendation functions for users by GLTC algorithm. Implementation of front-end functions of this system are based on Node.js, D3.js and Baidu map, and the trajectory data has been stored in MySQL database. We evaluate TaxiVis with the trajectory dataset collected from 6599 taxis in Kunming. Experimental results show that the system can effectively process and analyze trajectory data, and provide precise data supporting and presentation for the comprehensive evaluation of taxi operation efficiency and mining the drivers’ intelligence.

SPATIO-TEMPORAL PATTERN MINING ON TRAJECTORY DATA USING ARM

Abstract. Preliminary mobile was considered to be a device to make human connections easier. But today the consumption of this device has been evolved to a platform for gaming, web surfing and GPS-enabled application capabilities. Embedding GPS in handheld devices, altered them to significant trajectory data gathering facilities. Raw GPS trajectory data is a series of points which contains hidden information. For revealing hidden information in traces, trajectory data analysis is needed. One of the most beneficial concealed information in trajectory data is user activity patterns. In each pattern, there are multiple stops and moves which identifies users visited places and tasks. This paper proposes an approach to discover user daily activity patterns from GPS trajectories using association rules. Finding user patterns needs extraction of user’s visited places from stops and moves of GPS trajectories. In order to locate stops and moves, we have implemented a place recognition algorithm. After extraction of visited points an advanced association rule mining algorithm, called Apriori was used to extract user activity patterns. This study outlined that there are useful patterns in each trajectory that can be emerged from raw GPS data using association rule mining techniques in order to find out about multiple users’ behaviour in a system and can be utilized in various location-based applications.