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IDENTIFIKACIJA KRITIČNIH TOČK PROMETNIH NESREČ S SMRTNIM IZIDOM IN TEŽJIMI POŠKODBAMI V REPUBLIKI SLOVENIJI
PREBIL, KLEMEN (Author), Benčina, Jože (Mentor) More about this mentor... This link opens in a new window

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Abstract
Zaradi naraščanja obsega cestnega prometa in števila udeležencev v prometu prihaja do porasta števila prometnih nesreč. Prometne nesreče so vsakdanji pojav in so posledica različnih dejavnikov. Ravno zaradi raznovrstnih dejavnikov je smiselno preučiti prostorske vzorce prometnih nesreč ter na podlagi prostorske razporeditve predvideti kritične cestne odseke, ki predstavljajo konkretno izhodišče za ugotavljanje dejavnikov prometnih nesreč in s tem sprejetje relevantnih ukrepov prometne varnosti. Namen dela je preučiti stanje prometnih nesreč na območju Mestne občine Ljubljana ter predlagati postopek s katerim bi poudarili kritične množice prometnih nesreč in jih prikazali na zemljevidu. Po pregledu literature ni bilo mogoče zaslediti slovenskih raziskav, ki bi natančno vizualno prikazale kritične cestne odseke glede na pretekle prometne nesreče. Cilj magistrskega dela je s pomočjo kernelove metode in dejanskih prostorskih razporeditev prometnih nesreč identificirati tiste kritične odseke, ki predstavljajo največjo nevarnost za nastanek prometnih nesreč. Skladno z navedenim magistrsko delo predstavlja prispevek na področju uporabe javno dostopnih podatkov o prometnih nesrečah ter oblikovanje modela, ki obsega metodo in postopek obdelave podatkov za identifikacijo najnevarnejših cestnih odsekov ter tako predstavlja izhodišče za nadaljnje strokovne analize in znanstvene raziskave obravnavne problematike varnosti v cestnem prometu. Rezultati analize bodo koristni za prometne policijske enote, ki skrbijo za umirjanje prometa, načrtovalce in izvajalce ukrepov politik prometne varnosti, vzdrževalce cestnega omrežja pri načrtovanju sanacije nevarnih cestnih odsekov kot tudi za načrtovalce odsekov novih cest.

Language:Slovenian
Keywords:prometne nesreče, dejavniki prometnih nesreč, kritične točke, prostorske razporeditve, kernelova metoda, kritični odseki, javno dostopni podatki
Work type:Master's thesis/paper (mb22)
Organization:FU - Faculty of Administration
Year:2019
Views:329
Downloads:152
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Secondary language

Language:English
Title:IDENTIFICATION OF TRAFFIC ACCIDENTS HOTSPOTS AND SERIOUS INJURIES IN THE REPUBLIC OF SLOVENIA
Abstract:
Due to the increase in road traffic and traffic participants, the number of road traffic accidents has risen dramatically. Road accidents are a daily occurrence and are the result of various factors. Because of the great diversity of these factors it is essential to study spatial patterns of road accidents and predict road accident ‘hotspots’ accordingly. The purpose of this thesis is to study the occurrence of road accidents in the City Municipality of Ljubljana and suggest ways to highlight and map road accident-prone points. After having studied literature on road accidents, I came to the conclusion that no similar detailed research has been conducted in Slovenia. The objective of this study is to identify accident-intensive sections by using Kernel’s method and taking into account the actual spatial patterns of road accidents. This Master’s thesis suggests a simple and an efficient methodology for the identification of accident-intensive sections. Accordingly, it is a contribution to the field of accessing publicly available information on road traffic accidents. The thesis will also help design a model consisting of a method and procedure for processing information used for the identification of road accident hotspots, which will serve as a basis for further expert analysis and scientific researches in the field of road safety. The results of this analysis will be beneficial to traffic police, who assist with various operations aimed at traffic calming, planners and implementers of road safety measures as well as road maintenance workers and engineers when planning road repairs and constructing new roads.

Keywords:road traffic accidents, factors of road accidents, hotspots, spatial patterns, Kernel’s method, accident-intensive sections, publicly available information

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