SPATIO – TEMPORAL ANALYSIS OF ROAD ACCIDENTS IN POLAND
This article attempts to model the daily number of road accidents in Poland using data mining methods such as random forests and artificial neural networks. There was compared the network, which takes into account all the analyzed explanatory variables to the network with reduced the number of input...
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Format: | Article |
Language: | English |
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University of Gdansk
2015-12-01
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Series: | Contemporary Economy |
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Online Access: | http://www.wspolczesnagospodarka.pl/?p=1088 |
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author | Kinga Kądziołka |
author_facet | Kinga Kądziołka |
author_sort | Kinga Kądziołka |
collection | DOAJ |
description | This article attempts to model the daily number of road accidents in Poland using data mining methods such as random forests and artificial neural networks. There was compared the network, which takes into account all the analyzed explanatory variables to the network with reduced the number of input variables. The network with reduced set of input variables was characterized by a slightly lower average absolute percentage error on the test set than the network, which includes all explanatory variables. There was also identified clusters of poviats characterized by high risk of road accidents. |
first_indexed | 2024-04-13T08:44:22Z |
format | Article |
id | doaj.art-869e46922a8c45f3b3e15932a3dbeba3 |
institution | Directory Open Access Journal |
issn | 2082-677X 2082-677X |
language | English |
last_indexed | 2024-04-13T08:44:22Z |
publishDate | 2015-12-01 |
publisher | University of Gdansk |
record_format | Article |
series | Contemporary Economy |
spelling | doaj.art-869e46922a8c45f3b3e15932a3dbeba32022-12-22T02:53:45ZengUniversity of GdanskContemporary Economy2082-677X2082-677X2015-12-0164123134SPATIO – TEMPORAL ANALYSIS OF ROAD ACCIDENTS IN POLANDKinga Kądziołka0Prokuratura Okręgowa w KatowicachThis article attempts to model the daily number of road accidents in Poland using data mining methods such as random forests and artificial neural networks. There was compared the network, which takes into account all the analyzed explanatory variables to the network with reduced the number of input variables. The network with reduced set of input variables was characterized by a slightly lower average absolute percentage error on the test set than the network, which includes all explanatory variables. There was also identified clusters of poviats characterized by high risk of road accidents.http://www.wspolczesnagospodarka.pl/?p=1088road accidentneural networkrandom forestMoran statistic |
spellingShingle | Kinga Kądziołka SPATIO – TEMPORAL ANALYSIS OF ROAD ACCIDENTS IN POLAND Contemporary Economy road accident neural network random forest Moran statistic |
title | SPATIO – TEMPORAL ANALYSIS OF ROAD ACCIDENTS IN POLAND |
title_full | SPATIO – TEMPORAL ANALYSIS OF ROAD ACCIDENTS IN POLAND |
title_fullStr | SPATIO – TEMPORAL ANALYSIS OF ROAD ACCIDENTS IN POLAND |
title_full_unstemmed | SPATIO – TEMPORAL ANALYSIS OF ROAD ACCIDENTS IN POLAND |
title_short | SPATIO – TEMPORAL ANALYSIS OF ROAD ACCIDENTS IN POLAND |
title_sort | spatio temporal analysis of road accidents in poland |
topic | road accident neural network random forest Moran statistic |
url | http://www.wspolczesnagospodarka.pl/?p=1088 |
work_keys_str_mv | AT kingakadziołka spatiotemporalanalysisofroadaccidentsinpoland |