Detection of Road Accident Accumulation Zones with a Visual Analytics Approach

Ramos, Luís F.; Silva, Luís; Santos, Maribel Yasmina; Pires, João Moura

doi:10.1016/j.procs.2015.08.615

Cited by 18 publications

(8 citation statements)

References 4 publications

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“…For this reason, GVA pays special attention to the issues of collaboration, communication, and flexibility (Andrienko et al, 2007). Recent examples of GVA applications are the analysis of criminal activity (Rodrigo, 2020), human mobility (Zhang et al, 2019), and road accident accumulation zones (Ramos, Silva, Santos, & Pires, 2015).…”

Section: Geo-visual Analyticsmentioning

confidence: 99%

Combining favorability modeling with collaborative geo‐visual analysis to improve agricultural pest management

Garcia-Chapeton

Toxopeus

Olivero³

et al. 2020

Transactions in GIS

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A pest is an organism that conflicts with human welfare, because it may affect crops, stored products, animals, or people (Rafikov & Balthazar, 2005). It is important to highlight that an organism is considered a pest only when its abundance reaches a level that seriously affects human welfare (Herzfeld & Sargent, 2017). Such pest outbreaks can threaten local flora and fauna (Tepley, Juan Paritis, & Veblen, 2012), and especially some agricultural pests can cause damages with significant economic impact for producers and the food supply chain, possibly threatening

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Section: Geo-visual Analyticsmentioning

confidence: 99%

Combining favorability modeling with collaborative geo‐visual analysis to improve agricultural pest management

Garcia-Chapeton

Toxopeus

Olivero³

et al. 2020

Transactions in GIS

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“…Visual analytics effectively supports the understanding, awareness, and users' needs with database operations and computational methods so can visualize large amounts of data. Ramos et al (2015) in their research used visual analytics to combine data analysis techniques and interactive visualization to obtain information from extensive and complex data [6]. Visual analytics accommodates the exploration of spatial based temporal data through maps consisting of various variables and parameters for analysis.…”

Section: Related Workmentioning

confidence: 99%

Visual analytics for railway network in Java Island

Susanta¹

2019

Sixth Geoinformation Science Symposium

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Train is one of mass transportation that contains spatial and attribute data. Based on one of the development services of the 2030 national railway, the innovation of Indonesian Ministry of Transportation is focused on the ease of uses, namely the public, academics, and policy-makers to access data and information through web-based maps (WebGIS). between stations, as well as the location of stations and railway lines. The results of the data processing are maps presented in a dashboard map view with visual analytics features. The data are processed with QGIS then are visualized by Operations Dashboard for ArcGIS. Visual analytics method is used because the results can show the relationship between variables. It can be used as a reference for decision making to develop transportation infrastructure, especially of rail transportation. Maps and graphics are visually interactive so change one attribute can affect the other attribute display. The result of this research is named Railway Network in Java.

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“…Por otro lado, en el continente europeo se encontraron una mayor variedad de metodologías para la determinación de las zonas críticas de accidentalidad (Elvik, 2008) (Montella, 2010) (Alonso et al, 2012) (Ramos, Silva, Santos, & Pires, 2015). Elvik (2008) realizó un estudio de revisión bibliográfica financiado por la Comisión Europea sobre cómo se identifican los puntos negros en ocho países de este continente.…”

Section: Estado Del Arteunclassified

Accident severity levels and traffic signs interactions in state roads: a seemingly unrelated regression model in unbalanced panel data approach

Šarić

Zhu

et al. 2018

Accident Analysis & Prevention

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This study intended to investigate the interactions between accident severity levels and traffic signs in state roads located in Croatia, and explore the correlation within accident severity levels and heterogeneity attributed to unobserved factors. The data from 410 state roads between 2012 and 2016 were collected from Traffic Accident Database System maintained by the Republic of Croatia Ministry of the Interior. To address the correlation and heterogeneity, a seemingly unrelated regression (SUR) model in unbalanced panel data approach was proposed, in which the seemingly unrelated model addressed the correlation of residuals, while the panel data model accommodated the heterogeneity due to unobserved factors. By comparing the pooled, fixed-effects and random-effects SUR models, the random-effects SUR model showed priority to the other two. Results revealed that (1) low visibility and the number of invalid traffic signs per km increased the accident rate of material damage, death or injured; (2) average speed limit exhibited a high accident rate of death or injured; (3) the number of mandatory signs was more likely to reduce the accident rate of material damage, while the number of warning signs was significant for accident rate of death or injured.

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Detection of Road Accident Accumulation Zones with a Visual Analytics Approach

Cited by 18 publications

References 4 publications

Combining favorability modeling with collaborative geo‐visual analysis to improve agricultural pest management

Combining favorability modeling with collaborative geo‐visual analysis to improve agricultural pest management

Visual analytics for railway network in Java Island

Accident severity levels and traffic signs interactions in state roads: a seemingly unrelated regression model in unbalanced panel data approach

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