Tytuł:

Predicting road accident counts in Poland and the Czech Republic using neural network models

Tytuł odmienny:

Prognozowanie liczby wypadków drogowych w Polsce i Czechach przy użyciu modeli sieci neuronowych

Twórca:

Gorzelańczyk, Piotr ; Ližbetinová, Lenka ; Pečman, Jan

Temat:

road accident ; pandemic ; forecasting ; neural networks ; Poland ; Czech Republic

Opis:

Every year, there is a decline in the number of car accidents reported in Poland, the Czech Republic, and globally. While recent trends due to the pandemic have influenced these figures, the overall rate remains significant. Therefore, it is crucial to take measures aimed at reducing this number. The primary focus of this article is to analyze the traffic accident statistics for Poland and the Czech Republic. Annual data regarding traffic incidents in both countries has been scrutinized to achieve this. Projections for 2024 to 2030 have been developed based on police reports. Various neural network models were utilized to forecast the number of accidents. The findings indicate that the number of traffic incidents is likely to stabilize. This stabilization can be viewed in the context of the increasing number of vehicles on the roads and the expansion of new highways. Additionally, selecting sample sizes for training, testing, and validation is crucial in influencing the results. Forecasting the number of traffic accidents is important for environmental protection, as accidents can lead to air and water pollution and increase noise, negatively affecting human health and ecosystems.

Miejsce wydania:

Koszalin

Wydawca:

Politechnika Koszalińska

Data:

2024

Typ:

artykuł w czasopiśmie

Język:

eng

Jest częścią:

Rocznik Ochrona Środowiska. Vol. 26, s. 603-612

Prawa:

Biblioteka Politechniki Koszalińskiej

Prawa dostępu:

internet

Licencja:

Creative Commons BY-SA 4.0