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Wyświetlanie 1-2 z 2
Tytuł:
Investigating snowplow-related injury severity along mountainous roadway in Wyoming
Autorzy:
Haq, Muhammad Tahmidul
Reza, Imran
Ksaibati, Khaled
Powiązania:
https://bibliotekanauki.pl/articles/2204253.pdf
Data publikacji:
2023
Wydawca:
Fundacja Centrum Badań Socjologicznych
Tematy:
winter highway maintenance
snowplows
injury severity
mixed logit model
unobserved heterogeneity
Wyoming
environment
Opis:
Snow removal and deicing using snowplow trucks assist transportation agencies to enhance roadway safety and mobility. However, due to slower travel speeds during these operations, motorists often end up in crashes for poor visibility and disturbance of the snow. Despite the risk associated with snowplows, no previous study was found that exclusively investigate the factors associated with injury severity in snowplow-involved crashes. Therefore, this paper presents an extensive exploratory analysis and fills this knowledge gap by identifying the significant contributing factors affecting the occupant injury severity from the aspects of crashes with snowplow involvement. The study utilized eleven years (2010-2020) of historical snowplow-related crash data from Wyoming. Both the binary logit model and mixed binary logit model were developed to investigate the impacts of the various occupant, vehicle, crash, roadway, and environmental characteristics on the corresponding occupant injury severity. As one of the important findings from this research concludes that other vehicle drivers are more responsible than snowplow drivers contributing to more severe injuries in crashes involving snowplows. Recommendations suggested based on the modeling results are expected to help transportation agencies and policymakers take necessary actions in reducing snowplow-involved crashes by targeting appropriate strategies and proper resource allocation.
Źródło:
Journal of Sustainable Development of Transport and Logistics; 2023, 8, 1; 73--88
2520-2979
Pojawia się w:
Journal of Sustainable Development of Transport and Logistics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An assessment of machine learning and data balancing techniques for evaluating downgrade truck crash severity prediction in Wyoming
Autorzy:
Ampadu, Vincent-Michael Kwesi
Haq, Muhammad Tahmidul
Ksaibati, Khaled
Powiązania:
https://bibliotekanauki.pl/articles/2176018.pdf
Data publikacji:
2022
Wydawca:
Fundacja Centrum Badań Socjologicznych
Tematy:
crash severity
performance
extreme gradient boosting tree
adaptive boosting tree
random forest
gradient boost decision tree
adaptive synthetic algorithm
Opis:
This study involved the investigation of various machine learning methods, including four classification tree-based ML models, namely the Adaptive Boosting tree, Random Forest, Gradient Boost Decision Tree, Extreme Gradient Boosting tree, and three non-tree-based ML models, namely Support Vector Machines, Multi-layer Perceptron and k-Nearest Neighbors for predicting the level of severity of large truck crashes on Wyoming road networks. The accuracy of these seven methods was then compared. The Final ROC AUC score for the optimized random forest model is 95.296 %. The next highest performing model was the k-NN with 92.780 %, M.L.P. with 87.817 %, XGBoost with 86.542 %, Gradboost with 74.824 %, SVM with 72.648 % and AdaBoost with 67.232 %. Based on the analysis, the top 10 predictors of severity were obtained from the feature importance plot. These may be classified into whether safety equipment was used, whether airbags were deployed, the gender of the driver and whether alcohol was involved.
Źródło:
Journal of Sustainable Development of Transport and Logistics; 2022, 7, 2; 6--24
2520-2979
Pojawia się w:
Journal of Sustainable Development of Transport and Logistics
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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