Application of neural networks to forecast the number of road accidents in provinces in Poland

Gorzelańczyk, Piotr

doi:10.1016/j.heliyon.2022.e12767

Cited by 4 publications

(2 citation statements)

References 14 publications

(13 reference statements)

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“…Selected time series models and exponential models were used to forecast the number of accidents. Forecasting of the number of road accidents using factors influencing this value and the use of other forecasting methods (e.g., neural networks, linear regression) can be found in the author's other work in this area [49][50][51][52][53][54][55][56][57][58][59][60][61]. In their study, the authors used the police's statics data, not taking into account other factors affecting the occurrence of a traffic accident.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Number of Road Accidents in Poland by Road Type

Gorzelańczyk,

2024

Highlights Veh.

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On Polish highways, a staggering number of individuals pass away each year. The quantity is still quite large even if the value is declining year after year. The value of traffic accidents has greatly decreased due to the epidemic, but it is still quite high. In order to reduce this number, it is required to identify the roads where the majority of accidents occur and to understand the predicted number of accidents in the upcoming years. The article’s goal is to predict how many accidents will occur on Polish roads based on the kind of roads. To achieve this, monthly accident data for Poland from the Police’s statistics for the years 2007–2021 were analyzed, and a prediction for the years 2022–2024 was created. As is evident, either the number of accidents is rising or it is stabilizing. This is mostly caused by the rise in automobile traffic. Additionally, predictions indicate that given the existing circumstances, a significant rise in the number of accidents on Polish roads may be anticipated. This is especially evident in the nation’s growing number of freeways. It should be remembered that the current epidemic distorts the findings. Selected time series models were used in the investigation in Statistica.

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Section: Introductionmentioning

confidence: 99%

Forecasting the Number of Road Accidents in Poland by Road Type

Gorzelańczyk,

2024

Highlights Veh.

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“…2). The problem of traffic safety has been addressed in the following articles (Bartuska et al 2016, Čubranić--DobroDolac et al 2020, Gorzelanczyk, bazela 2021, Gorzelanczyk, Huk 2022, Gorzelanczyk, Tylicki 2023, Gorzelanczyk et al 2020, 2022a, 2022b, 2022c, 2022d, Gorzelanczyk 2023a, 2023b, 2023c, 2023d. A vector autoregression model has also been used to forecast the number of traffic accidents, the drawback of which is the need to have a large number of observations of variables in order to correctly estimate their parameters (wójcik 2014), as well as the autoregression models of moneDero et al (2021) for analyzing the number of fatalities (moneDero et al 2021) and the curve--fit regression models of al-maDani (2018).…”

Section: Introductionmentioning

confidence: 99%

Impact of information on the number of traffic accidents on the outcome of the forecast

Gorzelanczyk

2023

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Every year, more and more vehicles appear on the world's roads. This leads to increased traffic on the roads. Road accidents have become a rapidly growing threat. They cause loss of human life and economic assets. This is due to the rapid growth of the world's human population and the very rapid development of motorization. The main problem in forecasting and analyzing data on the number of traffic accidents is the small size of the dataset that can be used for analysis in this regard. And on the other hand, road accidents cause, globally, millions of deaths and injuries annually is their density in time and space. It is worth noting that the pandemic has reduced the number of traffic accidents. However, the value is still very high. The purpose of the article is to assess the impact of information on the number of traffic accidents on the outcome of the forecast. To this end, using historical statistical data, the forecast of the number of traffic accidents for the following years was determined, and how this variability of the input data affects the value of the average percentage error of the forecast was determined. Based on the study, it can be concluded that a smaller number of input data, historical data on the number of accidents, instead of 32 years, 7 years, makes the determination of the forecast of the number of accidents for subsequent years, is at a satisfactory level, the average absolute percentage error of MAPE less than 7%. The article concludes with the determination of the forecast for future years. It is worth noting that the prevailing pandemic distorts the results obtained.

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A Quantitative Approach to Road Safety in Morocco: Reducing Accidents through Predictive Modeling

Bel-lahcen,

Abdellaoui Alaoui,

Tékouabou Koumétio

et al. 2023

E3S Web Conf.

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This paper uses machine learning to predict road accidents in Morocco, a country marked by high annual accident rates. Our model employs data such as weather, time of day, and road conditions, derived from historical accidents and environmental records. Findings suggest that such predictive modeling can enable traffic authorities to anticipate high-risk situations and enact pre-emptive safety measures, contributing to significant reductions in road accidents. This study provides a data-driven approach towards policy implementation for road safety, with insights applicable to global road safety initiatives.

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Application of neural networks to forecast the number of road accidents in provinces in Poland

Cited by 4 publications

References 14 publications

Forecasting the Number of Road Accidents in Poland by Road Type

Forecasting the Number of Road Accidents in Poland by Road Type

Impact of information on the number of traffic accidents on the outcome of the forecast

A Quantitative Approach to Road Safety in Morocco: Reducing Accidents through Predictive Modeling

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