Forecasting the Number of Road Accidents in Poland Depending on the Day of the Week using Neural Networks

Gorzelańczyk, Piotr; Tylicki, H.

doi:10.2478/logi-2023-0004

Cited by 3 publications

(2 citation statements)

References 12 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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“…The goal of their study was to increase traffic fluency, safety, and environmental protection in urban areas. Neural networks has also been used in a study to forecast the number of road accidents and the authors opined that neural networks are an effective tool to study traffic scenarios [30]. In a dated study [31], the authors reviewed and suggested various traffic scenarios/contexts, which should be studied in detail; and one of them was to address the travel time by various categories of vehicles.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Impact of Three-Wheelers on Traffic Flow in India: A Case Study Using Ann

Mohanty,

Pattanaik,

Ranjan Samal

et al. 2024

Communications

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This study explores the significant role of three-wheelers (3W) on urban road traffic, focusing on the reduction in average vehicle speeds and its consequential impact on the Level of Service (LOS) of the road. Data collected from various traffic volumes reveal significant speed reductions ranging from 4 % to 35 % due to the movement of 3Ws on the road. To model this phenomenon, Artificial Neural Network (ANN) is employed. The resulting reductions in speed, induced by the 3W, trigger LOS degradation, leading to congestion and delays. A noteworthy finding is that the traffic flow naturally gravitates towards the LOS C, underscoring the substantial role played by the 3W in diminishing the overall LOS of the road. This research offers critical insights into the dynamics of urban traffic influenced by the 3W, providing a valuable foundation for traffic management and urban planning efforts.

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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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Forecasting the Number of Road Accidents in Poland Depending on the Day of the Week using Neural Networks

Cited by 3 publications

References 12 publications

Forecasting the Number of Road Accidents in Poland by Road Type

Forecasting the Number of Road Accidents in Poland by Road Type

Assessing the Impact of Three-Wheelers on Traffic Flow in India: A Case Study Using Ann

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

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