Matches in SemOpenAlex for { <https://semopenalex.org/work/W4280604456> ?p ?o ?g. }
Showing items 1 to 94 of
94
with 100 items per page.
- W4280604456 endingPage "6032" @default.
- W4280604456 startingPage "6032" @default.
- W4280604456 abstract "Identifying dangerous events from driving behavior data has become a vital challenge in intelligent transportation systems. In this study, we compared machine and deep learning-based methods for classifying the risk levels of near-crashes. A dataset was built for the study by considering variables related to naturalistic driving, temporal data, participants, and road geometry, among others. Hierarchical clustering was applied to categorize the near-crashes into several risk levels based on high-risk driving variables. The adaptive lasso variable model was adopted to reduce factors and select significant driving risk factors. In addition, several machine and deep learning models were used to compare near-crash classification performance by training the models and examining the model with testing data. The results showed that the deep learning models outperformed the machine learning and statistical models in terms of classification performance. The LSTM model achieved the highest performance in terms of all evaluation metrics compared with the state-of-the-art models (accuracy = 96%, recall = 0.93, precision = 0.88, and F1-measure = 0.91). The LSTM model can improve the classification accuracy and prediction of most near-crash events and reduce false near-crash classification. The finding of this study can benefit transportation safety in predicting and classifying driving risk. It can provide useful suggestions for reducing the incidence of critical events and forward road crashes." @default.
- W4280604456 created "2022-05-22" @default.
- W4280604456 creator A5001586388 @default.
- W4280604456 creator A5010991623 @default.
- W4280604456 creator A5061107528 @default.
- W4280604456 creator A5067803203 @default.
- W4280604456 creator A5080280865 @default.
- W4280604456 date "2022-05-16" @default.
- W4280604456 modified "2023-10-14" @default.
- W4280604456 title "Risk Levels Classification of Near-Crashes in Naturalistic Driving Data" @default.
- W4280604456 cites W1842501785 @default.
- W4280604456 cites W1972997465 @default.
- W4280604456 cites W1999845506 @default.
- W4280604456 cites W2046017949 @default.
- W4280604456 cites W2132424470 @default.
- W4280604456 cites W2283882366 @default.
- W4280604456 cites W2337815039 @default.
- W4280604456 cites W2411066234 @default.
- W4280604456 cites W2562005416 @default.
- W4280604456 cites W2591677859 @default.
- W4280604456 cites W2597665707 @default.
- W4280604456 cites W2734778015 @default.
- W4280604456 cites W2750591756 @default.
- W4280604456 cites W2809407956 @default.
- W4280604456 cites W2885460958 @default.
- W4280604456 cites W2897805291 @default.
- W4280604456 cites W2923142038 @default.
- W4280604456 cites W2935760944 @default.
- W4280604456 cites W2942081241 @default.
- W4280604456 cites W2963543439 @default.
- W4280604456 cites W2963856336 @default.
- W4280604456 cites W2979279607 @default.
- W4280604456 cites W2991137082 @default.
- W4280604456 cites W3008879069 @default.
- W4280604456 cites W3014168384 @default.
- W4280604456 cites W3017327217 @default.
- W4280604456 cites W3048065666 @default.
- W4280604456 cites W3092749388 @default.
- W4280604456 cites W3127587213 @default.
- W4280604456 cites W3134021547 @default.
- W4280604456 cites W3161134798 @default.
- W4280604456 cites W3162155731 @default.
- W4280604456 cites W3181189326 @default.
- W4280604456 cites W3181274447 @default.
- W4280604456 cites W3186086337 @default.
- W4280604456 doi "https://doi.org/10.3390/su14106032" @default.
- W4280604456 hasPublicationYear "2022" @default.
- W4280604456 type Work @default.
- W4280604456 citedByCount "4" @default.
- W4280604456 countsByYear W42806044562022 @default.
- W4280604456 countsByYear W42806044562023 @default.
- W4280604456 crossrefType "journal-article" @default.
- W4280604456 hasAuthorship W4280604456A5001586388 @default.
- W4280604456 hasAuthorship W4280604456A5010991623 @default.
- W4280604456 hasAuthorship W4280604456A5061107528 @default.
- W4280604456 hasAuthorship W4280604456A5067803203 @default.
- W4280604456 hasAuthorship W4280604456A5080280865 @default.
- W4280604456 hasBestOaLocation W42806044561 @default.
- W4280604456 hasConcept C108583219 @default.
- W4280604456 hasConcept C119857082 @default.
- W4280604456 hasConcept C154945302 @default.
- W4280604456 hasConcept C183469790 @default.
- W4280604456 hasConcept C199360897 @default.
- W4280604456 hasConcept C41008148 @default.
- W4280604456 hasConcept C73555534 @default.
- W4280604456 hasConcept C94124525 @default.
- W4280604456 hasConceptScore W4280604456C108583219 @default.
- W4280604456 hasConceptScore W4280604456C119857082 @default.
- W4280604456 hasConceptScore W4280604456C154945302 @default.
- W4280604456 hasConceptScore W4280604456C183469790 @default.
- W4280604456 hasConceptScore W4280604456C199360897 @default.
- W4280604456 hasConceptScore W4280604456C41008148 @default.
- W4280604456 hasConceptScore W4280604456C73555534 @default.
- W4280604456 hasConceptScore W4280604456C94124525 @default.
- W4280604456 hasIssue "10" @default.
- W4280604456 hasLocation W42806044561 @default.
- W4280604456 hasOpenAccess W4280604456 @default.
- W4280604456 hasPrimaryLocation W42806044561 @default.
- W4280604456 hasRelatedWork W2795261237 @default.
- W4280604456 hasRelatedWork W3014300295 @default.
- W4280604456 hasRelatedWork W3164822677 @default.
- W4280604456 hasRelatedWork W4223943233 @default.
- W4280604456 hasRelatedWork W4225161397 @default.
- W4280604456 hasRelatedWork W4312200629 @default.
- W4280604456 hasRelatedWork W4360585206 @default.
- W4280604456 hasRelatedWork W4364306694 @default.
- W4280604456 hasRelatedWork W4380075502 @default.
- W4280604456 hasRelatedWork W4380086463 @default.
- W4280604456 hasVolume "14" @default.
- W4280604456 isParatext "false" @default.
- W4280604456 isRetracted "false" @default.
- W4280604456 workType "article" @default.