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Compared with conventional travel data such as GPS data, detector data and float car data, call detail record data from the cell phone communication not only cost low but also has a large scale which demonstrate it is the best way to collect travel information for studying macroscopic travel activities. This paper presents a complete method to discover hot path and travel feature in a traffic network...
Intellectual learning systems (ITS) are integrate subsystems of users, learning content, learning strategies, communication, processing of heterogeneous data and information in their architecture. ITS are developed as part of a blended/hybrid design initiative to support learning processes. The concept of blended learning has been actively developing recently. Efforts in the field of development of...
Safety-oriented visualization is one of significant approaches to gain insights from time-spatial data while neural net currently serves as a decent way to perform machine learning in data mining industry. This paper proposes a visual analytics pipeline for trajectory data enabling better understanding movements pattern of people using Neural Network as back-end and other visualization techniques...
Analyzing and mining trajectories of moving objects (such as persons or vehicles) in the cities bring a promising way to discover the potential knowledge and therefore can foster diversified applications, including personalized travel services, intelligent transportation systems (ITSs), and etc. For increasing the intelligence of current public transit systems, the paper proposes to discover and utilize...
Collision-free is one of the major safety concerns for maritime traffic management. To analyze the collision data and understand the cause of the collision can contribute the improvement of the maritime traffic safety and management. However, the real collisions is not always available to analyze. Based on a massive AIS trajectory data collected, we focus on mining the ships' movement behaviors those...
Affective (emotional) responses contribute to student engagement and, ultimately, to the decision to persist or withdraw from Computer Science as a discipline of study. We investigate affective response as a factor contributing to engagement and professional identity formation during the university-to-work transition, as supported during a senior capstone project experience. By conducting qualitative...
The social network users always need personalized information. In the present scenario, people are not willing to spend a lot of time to specify their personal needs. So a system is needed which automatically suggest personalized things to users. The Recommendation system is a system which is used to recommend resources that user may be interested in by mining users' interests and/or preferences....
Chest tomosynthesis (CTS) is a newly developed imaging technique which provides pseudo-3D volume anatomical information of thorax from limited angle projections and therefore improves the visibility of anatomy without so much increase on radiation dose compared to the chest radiography (CXR). However, one of the relatively common problems in CTS is the respiratory motion of patient during image acquisition,...
Traffic data collection and analysis is essential for designing roundabouts and improving safety at roundabouts. One of the main interested traffic data for roundabouts is often gap size. However, manual collection of gaps size by human operators is extremely laborious. In this work, we developed a system/tool to allow data mining of the recorded videos for automated extraction of gap size. The developed...
The pervasiveness of GPS-equipped mobile devices has been nurturing an unprecedented amount of semanticsrich spatiotemporal data. The confluence of spatiotemporal and semantic information offers new opportunities for extracting valuable knowledge about people's behaviors, but meanwhile also introduces its unique challenges that render conventional spatiotemporal data mining techniques inadequate....
In this paper, we proposed new framework for human action representation, which leverages the strengths of convolutional neural networks (CNNs) and the linear dynamical system (LDS) to represent both spatial and temporal structures of actions in videos. We make two principal contributions: first, we incorporate image-trained CNNs to detect action clip concepts, which takes advantage of different levels...
In this paper an algorithm used for video matching process is discussed. This algorithm basically works on the principle of tie-point extraction. This algorithm is conceptualized to extract information from different video frames in the similar locations. This extracted information is called tie-points. Without the video matching process, most of the video processing applications cannot exist. A few...
The ability to accurately predict the movement trajectory of people holds potential benefits for many applications, such as aged care and retail. Such movement predictions rely on collecting and analyzing large amounts of positioning data from sensors. In this work, we describe new algorithms to mine and predict people's movement in an indoor environment. Movement patterns are mined from historical...
Sensing of urban bus travelling data based on data collection from the Internet enables researchers from different regions to focus on the same study objects and promote the development of corresponding theories and technology. In this study, seven processes running on two servers were employed to retrieve real-time bus travelling data from the official bus information inquiry website of Suzhou, China...
Robot manipulation is one of prerequisites capability for service robot. However, autonomous manipulation remains a challenging problem for robot to implement the task, where robot has physical interactions and mechanical contacts with its environment. To date, learning from demonstration (LFD) has been successfully applied to enable robot to acquire new manipulation skill. Researches on LFD mainly...
We present a scheme to predict micro-scale weather based on region trajectories extracted from meteorological radar data. Moving regions representing micro-scale severe weather are extracted from radar data and form into trajectories, and based on the formed trajectories, we predict where the regions will move and its future scope by the approach of linear regression, which leads to prediction of...
With the growing number of location-based SNS (Social Networking Service) users, the utilization of SNS data is getting more and more important. In this paper, we focus on the prediction of users' locations from location-based SNS data. The location-based SNS data consists of sequence of checkins which are too sparse to predict the users' locations. In our previous research we generated users' probability...
This paper introduces three interpolation methods that enrich complex evolving region trajectories that are captured every day from numerous ground-based and space-based solar observatories. The interpolation module takes a trajectory as its input and generates an enriched trajectory with interpolated time-geometry pairs. we created three different interpolation techniques that are: MBR-Interpolation...
We seek to extract and explore statistics that characterize New York City traffic flows based on 700 million taxi trips in the 2010–2013 New York City taxi data. This paper presents a two-part solution for intensive computation: space and time design considerations for estimating taxi trajectories with Dijkstra's algorithm, and job parallelization and scheduling with HTCondor. Our contribution is...
Travel time prediction is important for freight transportation companies. Accurate travel time prediction can help these companies make better planning and task scheduling. For several reasons, most companies are not able to obtain traffic flow data from traffic management authorities, but a large amount of trajectory data were collected everyday which has not been fully utilised. In this study, we...
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