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Analiza stanu bezpieczeństwa ruchu drogowego na autostradzie A1 na odcinku przebiegającym przez województwo kujawsko-pomorskie

Kempa J., Bebyn G.

Autobusy : technika, eksploatacja, systemy transportowe

2018

R. 19, nr 6

121--126, CD

W artykule omówiono stan bezpieczeństwa ruchu drogowego na autostradzie A1, na jej odcinku przebiegającym przez teren województwa kujawsko-pomorskiego. Przedstawiane dane dotyczą lat 2016-2017. Obecnie w Polsce autostrady charakteryzują się największymi wartościami ciężkości wypadków drogowych z wszystkich rodzajów dróg publicznych, o czym świadczą dane statystyczne Policji. Stąd niezbędne jest podjęcie skutecznych działań, których efektem musi być zdecydowana poprawa bezpieczeństwa ruchu drogowego na wszystkich autostradach w Polsce, w tym także na autostradzie A1, na jej przebiegu przez województwo kujawsko-pomorskie. W pracy przedstawiono strukturę rodzajową wypadków drogowych, czas ich powstania i okoliczności, stan nawierzchni spowodowany warunkami pogodowymi, a także przyczyny powstawania zdarzeń drogowych.

The article discusses the state of road traffic safety on the A1 motorway, on its section passing the territory of the Kuyavian-Pomeranian Voivodeship. The presented data concerns the years 2016-2017. As it is evidenced by statistical data prepared by Police, motorways are characterized by the highest level of accidents rates of fatalities and injuries from all types of public roads at present in Poland. There-fore, it is necessary to take effective actions to improve road safety on all motorways in Poland, including the A1 motor-way passing the Kuyavian-Pomeranian Voivodeship. The paper presents the generic structure of road accidents, the time of their occurrence and circumstances, conditions of the surface caused by weather, as well as the reasons road accidents occurs.

Non-parametric machine learning methods for evaluating the effects of traffic accident duration on freeways

Lee Y., Wei C.-H., Chao K.-C.

Archives of Transport

2017

Vol. 43, iss. 3

91--104

Traffic accidents usually cause congestion and increase travel-times. The cost of extra travel time and fuel consumption due to congestion is huge. Traffic operators and drivers expect an accurately forecasted accident duration to reduce uncertainty and to enable the implementation of appropriate strategies. This study demonstrates two non-parametric machine learning methods, namely the k-nearest neighbour method and artificial neural network method, to construct accident duration prediction models. The factors influencing the occurrence of accidents are numerous and complex. To capture this phenomenon and improve the performance of accident duration prediction, the models incorporated various data including accident characteristics, traffic data, illumination, weather conditions, and road geometry characteristics. All raw data are collected from two public agencies and were integrated and cross-checked. Before model development, a correlation analysis was performed to reduce the scale of interrelated features or variables. Based on the performance comparison results, an artificial neural network model can provide good and reasonable prediction for accident duration with mean absolute percentage error values less than 30%, which are better than the prediction results of a k-nearest neighbour model. Based on comparison results for circumstances, the Model which incorporated significant variables and employed the ANN method can provide a more accurate prediction of accident duration when the circumstances involved the day time or drunk driving than those that involved night time and did not involve drunk driving. Empirical evaluation results reveal that significant variables possess a major influence on accident duration prediction.