Title:

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

Alternative title:

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

Creator:

Gorzelańczyk, Piotr ; Ližbetinová, Lenka ; Pečman, Jan

Subject:

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

Description:

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.

Place of publishing:

Koszalin

Publisher:

Politechnika Koszalińska

Date:

2024

Type:

artykuł w czasopiśmie

Language:

eng

Is part of:

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

Rights:

Biblioteka Politechniki Koszalińskiej

Access rights:

internet

License:

Creative Commons BY-SA 4.0