FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3137520482> ?p ?o ?g. }

• W3137520482 endingPage "106094" @default.
• W3137520482 startingPage "106094" @default.
• W3137520482 abstract "Motorcycles and motorcyclists have a variety of attributes that have been found to be a potential contributor to the high liability of vulnerable road users (VRUs). Vulnerable Road Users (VRUs) that include pedestrians, bicyclists, cycle-rickshaw occupants, and motorcyclists constitute by far the highest share of road traffic accidents in developing countries. Motorized three-wheeled Rickshaws (3W-MR) is a popular public transport mode in almost all Pakistani cities and is used primarily for short trips to carry passengers and small-scale goods movement. Despite being an important mode of public transport in the developing world, little work has been done to understand the factors affecting the injury severity of three-wheeled motorized vehicles. Crash injury severity prediction is a promising research target in traffic safety. Traditional statistical models have underlying assumptions and predefined associations, which can yield misleading results if flouted. Machine learning(ML) is an emerging non-parametric method that can effectively capture the non-linear effects of both continuous and discrete variables without prior assumptions and achieve better prediction accuracy. This research analyzed injury severity of three-wheeled motorized rickshaws (3W-MR) using various machine learning-based identification algorithms, i.e., Decision jungle (DJ), Random Forest (RF), and Decision Tree (DT). Three years of crash data (from 2017 to 2019) was collected from Provincial Emergency Response Service RESCUE 1122 for Rawalpindi city, Pakistan. A total of 2,743 3W-MR crashes were reported during the study period that resulted in 258 fatalities. The predictive performance of proposed ML models was assessed using several evaluation metrics such as overall accuracy, macro-average precision, macro-average recall, and geometric means of individual class accuracies. Results revealed that DJ with an overall accuracy of 83.7 % outperformed the DT and RF-based on a stratified 10-fold cross-validation approach. Finally, Spearman correlation analysis showed that factors such as the lighting condition, crashes involving young drivers (aged 20–30 years), facilities with high-speed limits (over 60 mph), weekday, off-peak, and shiny weather conditions were more likely to worsen injury severity of 3W-MR crashes. The outcomes of this study could provide necessary and essential guidance to road safety agencies, particularly in the study area, for proactive implementation of appropriate countermeasures to curb road safety issues pertaining to three-wheeled motorized vehicles." @default.
• W3137520482 created "2021-03-29" @default.
• W3137520482 creator A5035565859 @default.
• W3137520482 creator A5069346865 @default.
• W3137520482 creator A5072860776 @default.
• W3137520482 creator A5087129173 @default.
• W3137520482 date "2021-05-01" @default.
• W3137520482 modified "2023-10-18" @default.
• W3137520482 title "A comparative study of machine learning classifiers for injury severity prediction of crashes involving three-wheeled motorized rickshaw" @default.
• W3137520482 cites W1965688470 @default.
• W3137520482 cites W1975654587 @default.
• W3137520482 cites W1975836306 @default.
• W3137520482 cites W1977619318 @default.
• W3137520482 cites W1979874437 @default.
• W3137520482 cites W1999595491 @default.
• W3137520482 cites W2018329558 @default.
• W3137520482 cites W2023131190 @default.
• W3137520482 cites W2030339471 @default.
• W3137520482 cites W2035089357 @default.
• W3137520482 cites W2044567263 @default.
• W3137520482 cites W2052501607 @default.
• W3137520482 cites W2056862038 @default.
• W3137520482 cites W2059591409 @default.
• W3137520482 cites W2059770536 @default.
• W3137520482 cites W2071074564 @default.
• W3137520482 cites W2075511117 @default.
• W3137520482 cites W2080911646 @default.
• W3137520482 cites W2082512129 @default.
• W3137520482 cites W2086927126 @default.
• W3137520482 cites W2088054590 @default.
• W3137520482 cites W2090428066 @default.
• W3137520482 cites W2092250615 @default.
• W3137520482 cites W2095760087 @default.
• W3137520482 cites W2115723325 @default.
• W3137520482 cites W2116978779 @default.
• W3137520482 cites W2124666446 @default.
• W3137520482 cites W2126859005 @default.
• W3137520482 cites W2164936698 @default.
• W3137520482 cites W2166642145 @default.
• W3137520482 cites W2168981938 @default.
• W3137520482 cites W2563798118 @default.
• W3137520482 cites W2610734451 @default.
• W3137520482 cites W2750591756 @default.
• W3137520482 cites W2770485688 @default.
• W3137520482 cites W2790026884 @default.
• W3137520482 cites W2890907316 @default.
• W3137520482 cites W2897805291 @default.
• W3137520482 cites W2901672822 @default.
• W3137520482 cites W2930108727 @default.
• W3137520482 cites W2936522960 @default.
• W3137520482 cites W2944077210 @default.
• W3137520482 cites W2947982105 @default.
• W3137520482 cites W2950883047 @default.
• W3137520482 cites W2951684046 @default.
• W3137520482 cites W2963830620 @default.
• W3137520482 cites W2967978287 @default.
• W3137520482 cites W2972488242 @default.
• W3137520482 cites W2998445090 @default.
• W3137520482 cites W3022730139 @default.
• W3137520482 cites W3036090384 @default.
• W3137520482 cites W3042932150 @default.
• W3137520482 cites W3043025514 @default.
• W3137520482 cites W3092807996 @default.
• W3137520482 doi "https://doi.org/10.1016/j.aap.2021.106094" @default.
• W3137520482 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33756425" @default.
• W3137520482 hasPublicationYear "2021" @default.
• W3137520482 type Work @default.
• W3137520482 sameAs 3137520482 @default.
• W3137520482 citedByCount "40" @default.
• W3137520482 countsByYear W31375204822021 @default.
• W3137520482 countsByYear W31375204822022 @default.
• W3137520482 countsByYear W31375204822023 @default.
• W3137520482 crossrefType "journal-article" @default.
• W3137520482 hasAuthorship W3137520482A5035565859 @default.
• W3137520482 hasAuthorship W3137520482A5069346865 @default.
• W3137520482 hasAuthorship W3137520482A5072860776 @default.
• W3137520482 hasAuthorship W3137520482A5087129173 @default.
• W3137520482 hasConcept C119857082 @default.
• W3137520482 hasConcept C127413603 @default.
• W3137520482 hasConcept C154945302 @default.
• W3137520482 hasConcept C157085824 @default.
• W3137520482 hasConcept C169258074 @default.
• W3137520482 hasConcept C183469790 @default.
• W3137520482 hasConcept C190385971 @default.
• W3137520482 hasConcept C199360897 @default.
• W3137520482 hasConcept C22212356 @default.
• W3137520482 hasConcept C3017944768 @default.
• W3137520482 hasConcept C41008148 @default.
• W3137520482 hasConcept C71924100 @default.
• W3137520482 hasConcept C84525736 @default.
• W3137520482 hasConcept C87833898 @default.
• W3137520482 hasConcept C99454951 @default.
• W3137520482 hasConceptScore W3137520482C119857082 @default.
• W3137520482 hasConceptScore W3137520482C127413603 @default.
• W3137520482 hasConceptScore W3137520482C154945302 @default.
• W3137520482 hasConceptScore W3137520482C157085824 @default.
• W3137520482 hasConceptScore W3137520482C169258074 @default.
• W3137520482 hasConceptScore W3137520482C183469790 @default.

• next