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AUTHOR(S):

Martin Uhl, Marco Riehle, Nico Kreuzer, Neha Sharma, Smita Taitwale, Dimple Pal, Jürgen Seitz

 

TITLE

Analysis of Road Accidents in Germany and Need for Sustainable Transport

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ABSTRACT

This study explores the state of a population sample that habitually used the resident, urban or regional public transport to travel to work in the Germany. Through an online data collected by German Federal Ministry of Transport and Digital Infrastructure on an annual time interval, it was possible to accumulate sociodemographic data to recognize the inclination to use public transport. With a research objective to show by reviewing different distributions of the data with unique identifier attribute, analyze if there are any special groups of participants that are more involved in the accident then other groups. The research excluded external independent variables like the weather and lighting conditions, that do not have major impacts on the road accidents. This paper presents the clear results by evaluating the data in correlation to the type, kind and category of the road accidents, which show that common accidents are during rush hours and have make up the biggest values in the dataset.

KEYWORDS

Accidents, Transport, Sustainability, Statistics, Logistic Regression, Decision Tree, Multinominal Naïve Bayes, Support Vector Machine, Random Forest, Open Access

 

Cite this paper

Martin Uhl, Marco Riehle, Nico Kreuzer, Neha Sharma, Smita Taitwale, Dimple Pal, Jürgen Seitz. (2021) Analysis of Road Accidents in Germany and Need for Sustainable Transport. International Journal of Transportation Systems, 6, 39-47

 

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