An Interactive Visual Analytics Platform for Smart Intelligent Transportation Systems Management

Kalamaras, Ilias; Zamichos, Alexandros; Salamanis, Athanasios; Drosou, Anastasios; Kehagias, Dionysios; Margaritis, Georgios; Papadopoulos, Stavros; Tzovaras, Dimitrios

doi:10.1109/tits.2017.2727143

Cited by 54 publications

(22 citation statements)

References 31 publications

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“…Aghabozorgi et al [12] state that density-based clustering has not been used broadly for time series data in the data mining community as it has some complexity. However, we found that many of our surveyed visual analytics papers have adopted density-based methods [34], [42], [48], [57], [64], [66], [67], [69], [71], [73], [74], [77], [78], [89]. Looking at combinations of visualization with clustering algorithms, the surveyed papers indicate that the trend is dominated by trajectory data that often adopts density-based techniques for clustering compared to other clustering algorithms.…”

Section: ) Density-based Methodsmentioning

confidence: 99%

“…It presents different places over time, providing a clear idea of spatio-temporal changes. A combination of interactive visualizations and automated analysis has together been shown to be an efficient approach in analyzing, tracking, and representing this type of data in order to understand and recognize the mobility of a diversity of moving objects, such as vehicles [64]- [71], and aircraft [66], [67], which can lead to path discovery, movement analysis, and location prediction.…”

Section: ) Time Series Of Spatial Positions Of Moving Objectsmentioning

confidence: 99%

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Clustering and Classification for Time Series Data in Visual Analytics: A Survey

et al. 2019

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Visual analytics for time series data has received a considerable amount of attention. Different approaches have been developed to understand the characteristics of the data and obtain meaningful statistics in order to explore the underlying processes, identify and estimate trends, make decisions and predict the future. The machine learning and visualization areas share a focus on extracting information from data. In this paper, we consider not only automatic methods but also interactive exploration. The ability to embed efficient machine learning techniques (clustering and classification) in interactive visualization systems is highly desirable in order to gain the most from both humans and computers. We present a literature review of some of the most important publications in the field and classify over 60 published papers from six different perspectives. This review intends to clarify the major concepts with which clustering or classification algorithms are used in visual analytics for time series data and provide a valuable guide for both new researchers and experts in the emerging field of integrating machine learning techniques into visual analytics.

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Section: ) Density-based Methodsmentioning

confidence: 99%

Section: ) Time Series Of Spatial Positions Of Moving Objectsmentioning

confidence: 99%

Clustering and Classification for Time Series Data in Visual Analytics: A Survey

et al. 2019

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“…Pace et al 's research [14] focuses on emerging healthcare industries. Smart transportation systems are another hot domain [15,16].…”

Section: Related Workmentioning

confidence: 99%

Edge Computing in an IoT Base Station System: Reprogramming and Real‐Time Tasks

Sun

et al. 2019

Complexity

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There are millions of base stations distributed across China, each containing many support devices and monitoring sensors. Conventional base station management systems tend to be hosted in the cloud, but cloud-based systems are difficult to reprogram and performing tasks in real-time is sometimes problematic, for example, sounding a combination of alarms or executing linked tasks. To overcome these drawbacks, we propose a hybrid edge-cloud IoT base station system, called BSIS. This paper includes a theoretical mathematical model that demonstrates the dynamic characteristics of BSIS along with a formulation for implementing BSIS in practice. Embedded programmable logic controllers serve as the edge nodes; a dynamic programming method creates a seamless integration between the edge nodes and the cloud. The paper concludes with a series of comprehensive analyses on scalability, responsiveness, and reliability. These analyses indicate a possible 60% reduction in the number of alarms, an edge response time of less than 0.1s, and an average downtime ratio of 0.66%.

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“…Smart transportation provides VA applications to visualise and analyse a large amount of data collected from transportation system, thereby helping in the improvement of the transportation systems in terms of minimizing traffic congestion, by providing alternative routes and reducing the number of accidents through the analyses of the history of mishaps, including factors such as their cause and the driver speed (Claramunt et al, 2000, Gröger and Plümer, 2012, Andrienko, 2013, Krüger et al, 2013, Objectives and Words, 2013, Exner, 2015, Yinhai Wang et al, 2016, Yun Wang et al, 2016, Kalamaras et al, 2017, Kurkcu et al, 2017. Singh et al, (2017) proposed a framework of interactive VA for detecting bike-riders without helmet, automatically in city traffic.…”

Section: Gv and Va In Smart Cities: Applicationsmentioning

confidence: 99%

Geo-Visualisation and Visual Analytics for Smart Cities: A Survey

Harbola

Coors

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Geo-Visualisation (GV) and Visual Analytics (VA) of geo-spatial data have become a focus of interest for research, industries, government and other organisations for improving the mobility, energy efficiency, waste management and public administration of a smart city. The geo-spatial data requirements, increasing volumes, varying formats and quality standards, present challenges in managing, storing, visualising and analysing the data. A survey covering GV and VA of the geo-spatial data collected from a smart city helps to portray the potential of such techniques, which is still required. Therefore, this survey presents GV and VA techniques for the geo-spatial urban data represented in terms of location, multi-dimensions including time, and several other attributes. Further, the current study provides a comprehensive review of the existing literature related to GV and VA from cities, highlighting the important open white spots for the cities’ geo-spatial data handling in term of visualisation and analytics. This will aid to get a better insight into the urban system and enable sustainable development of the future cities by improving human interaction with the geo-spatial data.</p>

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An Interactive Visual Analytics Platform for Smart Intelligent Transportation Systems Management

Cited by 54 publications

References 31 publications

Clustering and Classification for Time Series Data in Visual Analytics: A Survey

Clustering and Classification for Time Series Data in Visual Analytics: A Survey

Edge Computing in an IoT Base Station System: Reprogramming and Real‐Time Tasks

Geo-Visualisation and Visual Analytics for Smart Cities: A Survey

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