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Analysis of vehicle pedestrian crash severity using advanced machine learning techniques

Ul Arifeen Siyab, Ali Mujahid, Macioszek Elżbieta

Archives of Transport

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2023

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Vol. 68, iss. 4

91--116

EN

In 2015, over 17% of pedestrians were killed during vehicle crashes in Hong Kong while it raised to 18% from 2017 to 2019 and expected to be 25% in the upcoming decade. In Hong Kong, buses and the metro are used for 89% of trips, and walking has traditionally been the primary way to use public transportation. This susceptibility of pedestrians to road crashes conflicts with sustainable transportation objectives. Most studies on crash severity ignored the severity correlations between pedestrian-vehicle units engaged in the same impacts. The estimates of the factor effects will be skewed in models that do not consider these within-crash correlations. Pedestrians made up 17% of the 20,381 traffic fatalities in which 66% of the fatalities on the highways were pedestrians. The motivation of this study is to examine the elements that pedestrian injuries on highways and build on safety for these endangered users. A traditional statistical model's ability to handle misfits, missing or noisy data, and strict presumptions has been questioned. The reasons for pedestrian injuries are typically explained using these models. To overcome these constraints, this study used a sophisticated machine learning technique called a Bayesian neural network (BNN), which combines the benefits of neural networks and Bayesian theory. The best construction model out of several constructed models was finally selected. It was discovered that the BNN model outperformed other machine learning techniques like K-Nearest Neighbors, a conventional neural network (NN), and a random forest (RF) model in terms of performance and predictions. The study also discovered that the time and circumstances of the accident and meteorological features were critical and significantly enhanced model performance when incorporated as input. To minimize the number of pedestrian fatalities due to traffic accidents, this research anticipates employing machine learning (ML) techniques. Besides, this study sets the framework for applying machine learning techniques to reduce the number of pedestrian fatalities brought on by auto accidents.

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Doraźna i długotrwała pomoc psychologiczna ofiarom wypadków przy pracy

Warchał M., Warchał A.

Promotor BHP

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2016

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nr 12

24--26

PL

Formy pomocy psychologicznej uzależnione są od stanu poszkodowanego oraz rodzaju wypadku, jakiemu uległ pracownik. Często interwencji psychologicznej wymagają także świadkowie wypadku, dla których ma on również potencjalnie traumatyczny charakter. Jak powinna wyglądać pomoc psychologiczna po wypadku przy pracy?

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Road safety system in Poland

Żukowska J., Budzyński M.

Journal of KONBiN

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2008

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No. 1 (4)

491-505

EN

The main objective of the paper is to present crucial for road safety improvement elements of the diagnosis of safety state and safety system in Poland, which could be the part of the activities of ZEUS Project. The estimation of the diagnosis was made by the comparison with the standards from those countries, which are regarded as leaders in safety.

PL

Celem niniejszego artykułu jest przedstawienie kluczowych dla poprawy brd elementów diagnozy stanu i systemu bezpieczeństwa w Polsce, które jednocześnie mogłyby wejść w zakres działań objętych Projektem ZEUS. Ocenę diagnozy wykonano poprzez porównanie z wzorcami z tych krajów, które od lat uważa się za liderów w zakresie bezpieczeństwa.