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Crash Rate Estimation by Aerial Image Analysis

Kornfeld, Nils und Lücken, Leonhard und Leich, Andreas und Wagner, Peter und Saul, Hagen und Hoffmann, Ragna (2018) Crash Rate Estimation by Aerial Image Analysis. In: Proceedings of Expert Symposium on Accident Research (ESAR) 2018. Expert Symposium on Accident Research (ESAR) 2018, 19.-20. April 2018, Hannover.

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Kurzfassung

Estimating road safety is a major concern of a large body of theoretical research as well as for practitioners all over the world. Most related studies rely heavily on structured data as tables concerning the road geometry, infrastructural items, traffic volumes, etc., which are not always available. A more and more universally available source of data, which has rarely been used in conjunction with road safety research are aerial or satellite images. These images potentially contain a wealth of information relevant to the prediction of road safety if they could be thoroughly analyzed in great numbers. Coincident with the widespread availability of satellite and aerial images, machine learning algorithms for image processing and automatic object detection and classification are maturing. This allows the automated processing of huge amounts of image data by artificial neural networks (ANNs) or related machine learning systems, an area in which convolutional neural networks have shown a significant improvement over conventional methods. In the submitted work initial results on the application of machine learning on aerial images are presented. The goal is to determine an estimation of road safety levels. ANNs were trained to predict crash frequencies for road intersections relying merely on aerial images of the intersections. The used data consists of police recorded crashes in the city of Berlin and aerial images provided by the Berlin Senate Department for Urban Development. The performance of the ANN suggests that the line of research is worth further pursuit. For instance, the trained ANN was able to predict the presence of crashes on intersections in a Berlin district excluded from the training process with an accuracy of approximately 74%.

elib-URL des Eintrags:https://elib.dlr.de/120283/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:Crash Rate Estimation by Aerial Image Analysis
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Kornfeld, Nilsnils.kornfeld (at) dlr.dehttps://orcid.org/0000-0003-4889-363XNICHT SPEZIFIZIERT
Lücken, Leonhardleonhard.lücken (at) dlr.dehttps://orcid.org/0000-0001-6103-6531NICHT SPEZIFIZIERT
Leich, Andreasandreas.leich (at) dlr.dehttps://orcid.org/0000-0001-5242-2051NICHT SPEZIFIZIERT
Wagner, PeterPeter.Wagner (at) dlr.dehttps://orcid.org/0000-0001-9097-8026NICHT SPEZIFIZIERT
Saul, HagenHagen.Saul (at) dlr.dehttps://orcid.org/0000-0001-6961-7883NICHT SPEZIFIZIERT
Hoffmann, RagnaRagna.Hoffmann (at) dlr.dehttps://orcid.org/0000-0002-6607-5406NICHT SPEZIFIZIERT
Datum:2018
Erschienen in:Proceedings of Expert Symposium on Accident Research (ESAR) 2018
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
Status:veröffentlicht
Stichwörter:Machine Learning, Deep Learning, Image Classification, Regression
Veranstaltungstitel:Expert Symposium on Accident Research (ESAR) 2018
Veranstaltungsort:Hannover
Veranstaltungsart:internationale Konferenz
Veranstaltungsdatum:19.-20. April 2018
Veranstalter :Medizinische Hochschule Hannover, Accident Research Unit; Medizinische Hochschule Hannover Trauma Department; Per L. Reichertz Institut für Medizinische Informatik
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Verkehr
HGF - Programmthema:Straßenverkehr
DLR - Schwerpunkt:Verkehr
DLR - Forschungsgebiet:V ST Straßenverkehr
DLR - Teilgebiet (Projekt, Vorhaben):V - D.MoVe (alt)
Standort: Berlin-Adlershof
Institute & Einrichtungen:Institut für Verkehrssystemtechnik
Hinterlegt von: Kornfeld, Nils
Hinterlegt am:08 Jan 2020 14:27
Letzte Änderung:29 Mär 2023 00:37

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