An Adaptive Clustering Approach for Accident Prediction

Dadwal, Rajjat; Funke, Thorben; Demidova, Elena

doi:10.1109/itsc48978.2021.9564564

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…In their research they have found that the most significant factors for the accident prediction process are the time and nearby POIs. Recently, (Dadwal et al, 2021) proposed an adaptive clustering approach for the prediction of traffic accidents based on temporal and regional features. Moreover, they tested different clustering approaches for the spatial aggregation as well as additional prediction methods, which were applied to three German cities.…”

Section: Related Workmentioning

confidence: 99%

Spatial Analysis of External Influences on Traffic Accidents Using Open Data

Golze

Feuerhake

Sester

2022

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

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Abstract. In this article traffic accidents in Germany are investigated based on different environmental impacting factors. These factors are related to the accident surrounding like road characteristics, weather information, population density as well as POIs. For this purpose different open data source are used, like OpenStreetMap and an open weather API. These input data sources are processed in order to generate different categories of features, describing the on-site situation of an accident. Using the k-means clustering, six clusters were identified. In a consecutive investigation, each cluster is not only described with respect to the feature space but also in regards to their spatial distribution. Based on these findings, a regional comparison is done across the state boarders of Germany.

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Section: Related Workmentioning

confidence: 99%

Spatial Analysis of External Influences on Traffic Accidents Using Open Data

Golze

Feuerhake

Sester

2022

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

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“…Geographic data plays an essential role in a range of real-world applications on the Web, including machine learning models estimating travel time or charging demand for electric vehicles, recommending points of interest and predicting traffic accidents (e.g., [2], [9]). Such applications rely on rich representations of a variety of geographic entities including monuments, roads and charging stations.…”

Section: Introductionmentioning

confidence: 99%

“…-Geographic Web Information Sources such as OpenStreetMap (OSM 1 ) provide characteristics of geographic entities and their relationships. Today, OSM is an essential source of free and open geographic Web information created by voluntary effort, containing over 6.8 billion entities from 188 countries 2 .…”

Section: Introductionmentioning

confidence: 99%

Creating knowledge graphs for geographic data on the web

et al. 2022

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Geographic data plays an essential role in various Web, Semantic Web and machine learning applications. OpenStreetMap and knowledge graphs are critical complementary sources of geographic data on the Web. However, data veracity, the lack of integration of geographic and semantic characteristics, and incomplete representations substantially limit the data utility. Verification, enrichment and semantic representation are essential for making geographic data accessible for the Semantic Web and machine learning. This article describes recent approaches we developed to tackle these challenges.

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“…Geographic information is of crucial importance for a variety of real-world applications, including accident prediction (Dadwal et al 2021), detection of topological dependencies in road networks (Tempelmeier et al 2021a), and positioning charging stations (von Wahl et al 2022). Such applications can substantially profit from standardized machine-readable representations of geographic entities, including monuments, roads, and charging stations.…”

Section: Introductionmentioning

confidence: 99%

Volunteered Geographic Information

Yuan¹,

Chung²

2022

Encyclopedia of Tourism Management and Marketing

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An Adaptive Clustering Approach for Accident Prediction

Cited by 5 publications

References 15 publications

Spatial Analysis of External Influences on Traffic Accidents Using Open Data

Spatial Analysis of External Influences on Traffic Accidents Using Open Data

Creating knowledge graphs for geographic data on the web

Volunteered Geographic Information

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