InformaApp - Publikationen

DOI: 10.1109/ACIT49673.2020.9208843

Beitrag (Sammelband oder Tagungsband)

C. Hoermann, Raphaela Pagany, K. Kirchner, Wolfgang Dorner, M. Heurich, I. Storch

Predicting the risk of deer-vehicle collisions by inferring rules learnt from deer experience and movement patterns in the vicinity of roads

Proceedings of the 2020 10th International Conference on Advanced Computer Information Technologies (ACIT) [September 6-8, 2020; Deggendorf]

2020

Estimates of annual deer-vehicle collisions exceed one million incidences in Europe. Consequently, we were analyzing whether an animal’s experience and movement pattern close to roads can provide crucial information for accident prevention and mitigation measures. We applied an innovative approach using machine learning and step selection analyses to find rules and patterns in deer movement data for a better understanding of the spatio-temporal dynamics in wildlife-vehicle collisions. The rule tree indicated highest collision probabilities when the mean distance to a road of a roe deer tracking path was shorter than 192 meters and the roe deer crossed in more unfamiliar areas of its home range. The step selection function analysis revealed no obvious road avoidance and more road crossings in areas with less understory vegetation. Our results demonstrate the power of learned threshold values and step selection functions modelling results for a better understanding of the factors driving deer behavior in the vicinity of roads.

Raphaela Pagany, Javier Valdés, Wolfgang Dorner

Beitrag (Sammelband oder Tagungsband)

Risk prediction of wildlife-vehicle collisions comparing machine learning methods and data use

Proceedings of the 2020 10th International Conference on Advanced Computer Information Technologies (ACIT) [September 6-8, 2020; Deggendorf]

2020

DOI: 10.1109/ACIT49673.2020.9208946

Risk prediction of wildlife-vehicle collisions is crucial for reducing the abundant number of road accidents with wildlife worldwide. In this study, three different machine learning approaches - Gaussian Naive Bayes, Stochastic Gradient Descent, Random Forest - relying on recorded accident data, environmental, temporal and infrastructural parameters, are used to test the impurity of these parameters, and to predict the risk of wildlife-vehicle collisions. We use a dataset of one million police recorded accidents for southeastern Bavaria. Based on different approaches for feature engineering, we show the importance of data pre-processing and cleaning. With Random Forest, we receive a prediction accuracy of accidents of 86.7%. Additionally, the transferability of the three approaches is discussed, and possible ways of dynamic warnings via smartphone are presented.

Vortrag

C. Hoermann, Raphaela Pagany, K. Kirchner, Wolfgang Dorner, M. Heurich, I. Storch

Predicting the risk of deer-vehicle collisions by inferring rules learnt from deer experience and movement patterns in the vicinity of roads

2020 10th International Conference on Advanced Computer Information Technologies (ACIT), Deggendorf

2020

Raphaela Pagany, Javier Valdés, Wolfgang Dorner

Vortrag

Risk prediction of wildlife-vehicle collisions comparing machine learning methods and data use

2020 10th International Conference on Advanced Computer Information Technologies (ACIT), Deggendorf

2020

Zeitschriftenartikel

J. Trager, L. Kalová, Raphaela Pagany, Wolfgang Dorner

Warning apps for road safety – a technological and economical perspective for autonomous driving

[Article submitted]

International Journal of Human–Computer Interaction

2020

Zeitschriftenartikel

DOI: 10.1016/j.biocon.2020.108758

Wildlife-vehicle collisions—Influencing factors, data collection and research methods

Biological Conservation, vol. 251, no. November

2020

Wildlife-vehicle collisions (WVCs) are caused by the close interaction of human and wildlife habitats worldwide. The large number of globally distributed accidents and the variety of environmental impacts characterize WVCs as intricate and challenging to predict. However, numerous research studies have been conducted to understand the causal relationships between drivers, animals, and the environment. In this paper, 645 publications are reviewed to provide an overview and a wide-ranging knowledge about WVC research. The study gathers the influencing factors on WVCs, systematizes the approaches for data collection, and identifies the main developments in analysis and predicting methods for WVCs. Factors such as the proximity to forest, a gentle topography with sparsely curves, street width, and seasonal differences are common denominators for WVCs - independent of the species -, while traffic volume, the distance to urban areas, or road accompanying infrastructure are not clearly assignable influencing or non-influencing factors. Different ways of data collection are observed, which range from carcass surveys by ecologists or crowdsourcing for species conservation to nearly real-time official reporting by involved parties as a basis for driving safety. Data collection and quality are discussed for their applicability, in particular, regarding the currently used analysing approaches for WVCs. Additionally, the advantages of the rarely employed machine learning approaches are discussed in terms of dynamic WVC risk prediction - including large-scale and temporally unrestricted transferability. These approaches may be helpful for prospective warning and road safety management on a global scale.

Vortrag

Spatiotemporal risk prediction of wildlife-vehicle collisions

IENE - Infrastructure and Ecology Network Europe, Évora, Portugal (conference postponed)

2020

Vortrag

Risk prediction on road—Exemplified for wildlife-vehicle collisions

ESAR - Expert Symposium on Accident Research, Hannover (conference postponed)

2020

Vortrag

C. Hoermann, Raphaela Pagany, F. Ossi, F. Cagnacci, Wolfgang Dorner, M. Heurich

L‘ecologia del movimento e gli investimenti stradali: Un‘analisi a larga scala sul capriolo europeo (Capreolus capreolus)

Workshop Wildunfälle in Südtirol - Incidenti stradali con la fauna selvatica in Alto Adige, Bozen, Italy

2019

Vortrag

Simon Graf, Raphaela Pagany, Wolfgang Dorner, A. Weigold

Georeferencing of Road Infrastructure from Photographs using Computer Vision and Deep Learning for Road Safety Applications

5th International Conference on Geographical Information Systems Theory, Applications and Management, Heraklion, Greece

2019

Raphaela Pagany, Alexander Faschingbauer, Simon Graf, Wolfgang Dorner

Vortrag

Geoanalyse mit Methoden der Künstlichen Intelligenz - Datenaufbereitung, Homogenisierung und Anwendung am Fallbeispiel der (Wild-)Unfallprognose

Posterpräsentation

Fachsymposium Mobilität der Zukunft, Deggendorf

2019

Raphaela Pagany, Alexander Faschingbauer, Simon Graf, Wolfgang Dorner

Vortrag

Geoanalyse mit Methoden der Künstlichen Intelligenz - Datenaufbereitung, Homogenisierung und Anwendung am Fallbeispiel der (Wild-)Unfallprognose

Posterpräsentation

AGIT Symposium & Expo 2019 - Angewandte Geoinformatik, Salzburg, Österreich

2019

Digital Mobil Elektrotechnik und Medientechnik TC Freyung

Beitrag (Sammelband oder Tagungsband)

Simon Graf, Raphaela Pagany, Wolfgang Dorner, A. Weigold

Georeferencing of road infrastructure from photographs using computer vision and deep learning for road safety applications

Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM) [May 3-5, 2019; Heraklion, Greece], vol. Vol. 1

2019

DOI: 10.5220/0007706800710076

Raphaela Pagany, Anna Marquardt, Roland Zink

Zeitschriftenartikel

Electric Charging Demand Location Model—A User- and Destination-Based Locating Approach for Electric Vehicle Charging Stations

Sustainability, vol. 11, no. 8

2019

DOI: 10.3390/su11082301

In recent years, with the increased focus on climate protection, electric vehicles (EVs) have become a relevant alternative to conventional motorized vehicles. Even though the market share of EVs is still comparatively low, there are ongoing considerations for integrating EVs in transportation systems. Along with pushing EV sales numbers, the installation of charging infrastructure is necessary. This paper presents a user- and destination-based approach for locating charging stations (CSs) for EVs—the electric charging demand location (ECDL) model. With regard to the daily activities of potential EV users, potential positions for CSs are derived on a micro-location level in public and semipublic spaces using geographic information systems (GIS). Depending on the vehicle users’ dwell times and visiting frequencies at potential points of interest (POIs), the charging demand at such locations is calculated. The model is mainly based on a survey analyzing the average time spent per daily activity, regional data about driver and vehicle ownership numbers, and the georeferenced localization of regularly visited POIs. Optimal sites for parking and charging EVs within the POIs neighborhood are selected based on walking distance calculations, including spatial neighborhood effects, such as the density of POIs. In a case study in southeastern Germany, the model identifies concrete places with the highest overall demand for CSs, resulting in an extensive coverage of the electric energy demand while considering as many destinations within the acceptable walking distance threshold as possible.

Raphaela Pagany, Wolfgang Dorner

Zeitschriftenartikel

Do Crash Barriers and Fences Have an Impact on Wildlife-Vehicle Collisions? An Artificial Intelligence and GIS-Based Analysis

ISPRS International Journal of Geo-Information, vol. 8, no. 2

2019

DOI: 10.3390/ijgi8020066

Digital Mobil Elektrotechnik und Medientechnik TC Freyung

Wildlife–vehicle collisions (WVCs) cause significant road mortality of wildlife and have led to the installation of protective measures along streets. Until now, it has been difficult to determine the impact of roadside infrastructure that might act as a barrier for animals. The main deficits are the lack of geodata for roadside infrastructure and georeferenced accidents recorded for a larger area. We analyzed 113 km of road network of the district Freyung-Grafenau, Germany, and 1571 WVCs, examining correlations between the appearance of WVCs, the presence or absence of roadside infrastructure, particularly crash barriers and fences, and the relevance of the blocking effect for individual species. To receive infrastructure data on a larger scale, we analyzed 5596 road inspection images with a neural network for barrier recognition and a GIS for a complete spatial inventory. This data was combined with the data of WVCs in GIS to evaluate the infrastructure’s impact on accidents. The results show that crash barriers have an effect on WVCs, as collisions are lower on roads with crash barriers. In particular, smaller animals have a lower collision share. The risk reduction at fenced sections could not be proven as fenced sections are only available at 3% of the analyzed roads. Thus, especially the fence dataset must be validated by a larger sample number. However, these preliminary results indicate that the combination of artificial intelligence and GIS may be used to analyze and better allocate protective barriers or to apply it in alternative measures, such as dynamic WVC risk-warning.

Zeitschriftenartikel

Raphaela Pagany, Wolfgang Dorner

Achtung Wildwechsel! Wie ein dynamische GIS-Lösung hilft, Unfälle zu verhindern

arcAKTUELL: Operational Intelligence - Vorausschauend planen, no. 3, pp. 60-61

2018

Digital Mobil Elektrotechnik und Medientechnik TC Freyung

Vortrag

WilDa - Dynamische Wildunfallwarnung mit Hilfe von Verkehrs-, Unfall- und Umweltdaten

4. Jahreskonferenz des Netzwerks INDIGO (Internet und Digitalisierung Ostbayern), Deggendorf

2018

Vortrag

C. Hoermann, Raphaela Pagany, Wolfgang Dorner, M. Heurich

Experience of roe deer individuals helps to prevent traffic accidents

BioMove Symposium 2018: Integrating Biodiversity Research with Movement Ecology, Potsdam

2018

Vortrag

C. Hoermann, Raphaela Pagany, Wolfgang Dorner, M. Heurich

Movement ecology meets roads

48th Annual Meeting of the Ecological Society of Germany, Austria and Switzerland, Wien, Österreich

2018

Raphaela Pagany, Luis Ramirez Camargo, Wolfgang Dorner

Zeitschriftenartikel

A review of spatial localization methodologies for the electric vehicle charging infrastructure

International Journal of Sustainable Transportation, pp. 1-17

2018

DOI: 10.1080/15568318.2018.1481243