Autor: Ampadu, Vincent-Michael Kwesi - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Automating the updated grade severity rating system (GSRS) using the Visual Basic.net programming language
Autorzy:: Ampadu, Vincent-Michael Kwesi
Ksaibati, Khaled
Powiązania:: https://bibliotekanauki.pl/articles/2176016.pdf
Data publikacji:: 2022
Wydawca:: Fundacja Centrum Badań Socjologicznych
Tematy:: updated GSRS
longitudinal downgrades
object-oriented programming
Visual Basic.net programming language
Opis:: Truck crashes on steep downgrades due to excessive brake heating, resulting from brake applications to control speeding, are a continuing cause of concern for the Wyoming Department of Transportation (WYDOT). In 2016, WYDOT funded a project to update the existing Grade Severity Rating System. Furthermore, in 2020, WYDOT commissioned a research project to automate the updated version of the mathematical model through an interactive, intuitive, aesthetically appealing and user-friendly Visual Basic.net objected-oriented software to simplify the computation of the maximum safe descent speed on these downgrades based on the truck weight. The software provides functionality for both the continuous Slope and separate downgrade methods. The primary beneficiaries of this software will be the highway agencies who will be able to estimate the maximum safe speed of descent for trucks with various weight categories and hence produce Weight Specific Speed (WSS) signs for each downgrade or a multigrade section.
Źródło:: Journal of Sustainable Development of Transport and Logistics; 2022, 7, 2; 53--68
2520-2979
Pojawia się w:: Journal of Sustainable Development of Transport and Logistics
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Performance and cost-effectiveness of air disc brakes and air drum brakes for truck semi-trailers in different road and speed conditions
Autorzy:: Ampadu, Vincent-Michael Kwesi
Alrejjal, Anas
Ksaibati, Khaled
Powiązania:: https://bibliotekanauki.pl/articles/2204251.pdf
Data publikacji:: 2023
Wydawca:: Fundacja Centrum Badań Socjologicznych
Tematy:: GSRS
braking systems
grades
speeds
life cycle cost analysis
brake torque capacity
Opis:: This study uses TruckSim™ to model disc brakes and drum brakes on a fully loaded truck semi-trailer to study the performance of each brake type as downgrades and speeds vary. The brake performance is measured based on braking distance. A simplified economic comparison based on life cycle cost analysis to determine which road and vehicle conditions give rise to the cost-effectiveness of disc brakes is performed. The studies suggest that disc brakes shorten braking distances by 10-20%. They also suggest that the percentage reduction in braking distance as speed increases and downgrade gets steeper is approximately 12-19%. Evidence is provided that trucking companies operating their vehicles in steep terrain and at high speeds with disc brakes could benefit from 12-80% in long-term cost savings. Finally, at the societal level, by preventing crashes arising from rear-end collisions and runaway truck incidents, disc brakes save at least $649 million annually.
Źródło:: Journal of Sustainable Development of Transport and Logistics; 2023, 8, 1; 24--42
2520-2979
Pojawia się w:: Journal of Sustainable Development of Transport and Logistics
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

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

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