Matches in SemOpenAlex for { <https://semopenalex.org/work/W4295926836> ?p ?o ?g. }
- W4295926836 endingPage "29" @default.
- W4295926836 startingPage "1" @default.
- W4295926836 abstract "The aim of the current research was to develop models to predict the severity of accidents on rural roads in Tehran province, Iran. In this regard, using accident data from 2017 to 2020, the machine learning algorithms, including multiple logistic regression, multilayer perceptron neural network (MLPNN), and radial basis function neural network (RBFNN) models, as well as statistical methods, including Kolmogorov–Smirnov test, Friedman test, and factor analysis, were implemented to determine the contributory factors in the severity of accidents. Thus, nine variables affecting the severity of accidents were considered in modeling, and then the effect of each variable was calculated. By comparing the results of artificial neural network (ANN) models and the Friedman test, it was indicated that the human factor had a remarkable effect on accident severity. In addition, both machine learning and statistical methods can be served as guidance for safety authorities to provide safety solutions, thereby leading to reducing accidents. Finally, the performances of ANN models were analyzed by other mathematical models built by MATLAB programming." @default.
- W4295926836 created "2022-09-16" @default.
- W4295926836 creator A5023404767 @default.
- W4295926836 creator A5037067285 @default.
- W4295926836 creator A5086811169 @default.
- W4295926836 creator A5087100361 @default.
- W4295926836 creator A5087811897 @default.
- W4295926836 date "2022-09-14" @default.
- W4295926836 modified "2023-10-01" @default.
- W4295926836 title "Presentation of Machine Learning Approaches for Predicting the Severity of Accidents to Propose the Safety Solutions on Rural Roads" @default.
- W4295926836 cites W1227374044 @default.
- W4295926836 cites W1827495041 @default.
- W4295926836 cites W1964079382 @default.
- W4295926836 cites W1983242123 @default.
- W4295926836 cites W2021327913 @default.
- W4295926836 cites W2028282328 @default.
- W4295926836 cites W2060527484 @default.
- W4295926836 cites W2066816378 @default.
- W4295926836 cites W2081762866 @default.
- W4295926836 cites W2106949108 @default.
- W4295926836 cites W2170465906 @default.
- W4295926836 cites W2258201207 @default.
- W4295926836 cites W2275318721 @default.
- W4295926836 cites W2325851251 @default.
- W4295926836 cites W2467529412 @default.
- W4295926836 cites W2534749128 @default.
- W4295926836 cites W2562005416 @default.
- W4295926836 cites W2604806085 @default.
- W4295926836 cites W2612843093 @default.
- W4295926836 cites W2626467709 @default.
- W4295926836 cites W2768493425 @default.
- W4295926836 cites W2783628520 @default.
- W4295926836 cites W2790921206 @default.
- W4295926836 cites W2801640587 @default.
- W4295926836 cites W2885114451 @default.
- W4295926836 cites W2891740026 @default.
- W4295926836 cites W2896124512 @default.
- W4295926836 cites W2898887680 @default.
- W4295926836 cites W2904262414 @default.
- W4295926836 cites W2910156658 @default.
- W4295926836 cites W2912129959 @default.
- W4295926836 cites W2922613984 @default.
- W4295926836 cites W2943827903 @default.
- W4295926836 cites W2974120116 @default.
- W4295926836 cites W2980064426 @default.
- W4295926836 cites W2997352570 @default.
- W4295926836 cites W3005489823 @default.
- W4295926836 cites W3006171834 @default.
- W4295926836 cites W3032842701 @default.
- W4295926836 cites W3039738118 @default.
- W4295926836 cites W3047926331 @default.
- W4295926836 cites W3080610273 @default.
- W4295926836 cites W3080869053 @default.
- W4295926836 cites W3085071513 @default.
- W4295926836 cites W3093884784 @default.
- W4295926836 cites W3095005385 @default.
- W4295926836 cites W3107487429 @default.
- W4295926836 cites W3109024892 @default.
- W4295926836 cites W3114297766 @default.
- W4295926836 cites W3119436968 @default.
- W4295926836 cites W3121592851 @default.
- W4295926836 cites W3123989985 @default.
- W4295926836 cites W3126641280 @default.
- W4295926836 cites W3128599211 @default.
- W4295926836 cites W3129592173 @default.
- W4295926836 cites W3130011030 @default.
- W4295926836 cites W3131813263 @default.
- W4295926836 cites W3131927077 @default.
- W4295926836 cites W3133428805 @default.
- W4295926836 cites W3134770171 @default.
- W4295926836 cites W3139170758 @default.
- W4295926836 cites W3149852514 @default.
- W4295926836 cites W3152725669 @default.
- W4295926836 cites W3156554944 @default.
- W4295926836 cites W3160920078 @default.
- W4295926836 cites W3161116672 @default.
- W4295926836 cites W3162196123 @default.
- W4295926836 cites W3164690557 @default.
- W4295926836 cites W3166361176 @default.
- W4295926836 cites W3171093259 @default.
- W4295926836 cites W3175531543 @default.
- W4295926836 cites W3181990550 @default.
- W4295926836 cites W3191688278 @default.
- W4295926836 cites W3193498964 @default.
- W4295926836 cites W3194479518 @default.
- W4295926836 cites W3199877420 @default.
- W4295926836 cites W3203053623 @default.
- W4295926836 cites W3204766889 @default.
- W4295926836 cites W3205808861 @default.
- W4295926836 cites W3208947677 @default.
- W4295926836 cites W3209257685 @default.
- W4295926836 cites W3210701025 @default.
- W4295926836 cites W3213300473 @default.
- W4295926836 cites W3214389062 @default.
- W4295926836 cites W3214952236 @default.
- W4295926836 cites W3217272371 @default.
- W4295926836 cites W4200254172 @default.
- W4295926836 cites W4205442569 @default.