ICE--visual analytics for transportation incident datasets

Pack, Michael L.; Wongsuphasawat, Krist; VanDaniker, Michael; Filippova, Darya

doi:10.1109/iri.2009.5211551

Cited by 22 publications

(12 citation statements)

References 6 publications

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“…Two variants of STC that illustrate related properties of traffic data: (a) GeoTime [43] in which events, objects, and activities are represented as the 3-D trajectories; (b) stack-based visualization [13], in which trajectories are stacked together. Many traffic data visualization applications [26] employ parallel coordinate plot (PCP) [90] to show multiple attributes [ Fig. 14(a)].…”

Section: • Textual Propertiesmentioning

confidence: 99%

“…They cover situation-aware exploration and prediction [5], [14], [75], [91], [94], pattern discovery and clustering [9], [23], [34]- [36], [68], [92], and traffic situation monitoring [7], [11], [24], [26], [27], [93]. In this section, we present representative works, and summarize these works in Table III.…”

Section: Visual Analysis Of Traffic Datamentioning

confidence: 99%

“…18. ICE is a visually assisted application for studying transportation incident data [26]. the documents, namely, taxi topics that disclose interesting movement patterns of taxis.…”

Section: B Pattern Discovery and Clusteringmentioning

confidence: 99%

“…Incident Cluster Explorer (ICE) is an application that studies transportation incident datasets [26] (Fig. 18).…”

Section: Visual Monitoring Of Traffic Situationsmentioning

confidence: 99%

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A Survey of Traffic Data Visualization

Chen

Guo

Wang

2015

IEEE Trans. Intell. Transport. Syst.

249

137

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Data-driven intelligent transportation systems utilize data resources generated within intelligent systems to improve the performance of transportation systems and provide convenient and reliable services. Traffic data refer to datasets generated and collected on moving vehicles and objects. Data visualization is an efficient means to represent distributions and structures of datasets and reveal hidden patterns in the data. This paper introduces the basic concept and pipeline of traffic data visualization, provides an overview of related data processing techniques, and summarizes existing methods for depicting the temporal, spatial, numerical, and categorical properties of traffic data.

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Section: • Textual Propertiesmentioning

confidence: 99%

Section: Visual Analysis Of Traffic Datamentioning

confidence: 99%

“…18. ICE is a visually assisted application for studying transportation incident data [26]. the documents, namely, taxi topics that disclose interesting movement patterns of taxis.…”

Section: B Pattern Discovery and Clusteringmentioning

confidence: 99%

“…Incident Cluster Explorer (ICE) is an application that studies transportation incident datasets [26] (Fig. 18).…”

Section: Visual Monitoring Of Traffic Situationsmentioning

confidence: 99%

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A Survey of Traffic Data Visualization

Chen

Guo

Wang

2015

IEEE Trans. Intell. Transport. Syst.

249

137

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“…Conventional approaches that explore transportation data [12] have built tools for examining historical incidents such as accidents. These tools analyze periodically updated archived data and separate data collection from exploration.…”

Section: Conventional Approachesmentioning

confidence: 99%

A Smartphone-based System for Automated Congestion Prediction

Fiondella

Gokhale

Lownes

2015

International Conferences on Software Engineering and Knowledge Engineering

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Accurate collection of traffic data is essential, tactically for efficient highway operations and strategically for capacity planning purposes. Currently, this traffic data is collected by physical sensors, which suffers from many limitations. These sensors gather limited measurements of vehicle speeds and times from their fixed locations and cannot systematically acquire mobility dynamics of individual vehicles and the interactions among them. Also, because deploying and maintaining physical sensors is expensive and time consuming, typically they are installed to measure traffic only on the busier road segments within a transportation network. To overcome these issues, this paper reports on a general-purpose, location-aware, smartphone-based traffic monitoring system as a simple and an inexpensive alternative to collect dynamic vehicle data extensively through a transportation network. A privacy-preserving smartphone application was developed, deployed, and tested on the network surrounding the University of Connecticut (UConn) in Storrs. It securely transmits individual trip data over the Internet to the servers hosted at UConn. Preliminary experimentation suggests that crowdsourcing the collection of traffic data with smartphones can be cost effective and can lead to richer data sets spanning the entire web of roadways.

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Big Data Analytics and Visualization with Spatio-Temporal Correlations for Traffic Accidents

Fan

Wang

et al. 2015

Lecture Notes in Computer Science

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ICE--visual analytics for transportation incident datasets

Cited by 22 publications

References 6 publications

A Survey of Traffic Data Visualization

A Survey of Traffic Data Visualization

A Smartphone-based System for Automated Congestion Prediction

Big Data Analytics and Visualization with Spatio-Temporal Correlations for Traffic Accidents

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