FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4285384612> ?p ?o ?g. }

• W4285384612 abstract "Safety performance functions (SPFs) are the key regression tools in the road safety management process (RSMP) and are used to predict crash frequency given a set of roadway and traffic factors. Although regression-based SPFs have been proven to be reliable tools for road safety predictive analytics, some limitations and constrains have been highlighted in the literature, such as the need to assume a probability distribution, the need to select a predefined functional form, possible correlation between independent variables, and possible transferability issues. An alternative to traditional regression models as predictive tools is the use of machine learning (ML) algorithms. This research compared the prediction performance of three well-known ML algorithms, i.e., support vector machine (SVM), decision tree (DT), and random forest (RF), with that of traditional SPFs, and applied and validated ML algorithms in network screening, which is the first step in the RSMP. To achieve these objectives, traditional SPFs using negative-binomial (NB) generalized linear regression were estimated and compared with ML algorithms using three different goodness-of -fit criteria. A data set of urban signalized and unsignalized intersections from two major municipalities in Saskatchewan (Canada) was considered as a case study. Ranking consistency tests of collision-prone locations identified using ML-based and SPF-based performance measures were conducted. The results showed that the consistency of ML-based measures in identifying hotspots was comparable to that of SPF-based measures, particularly the excess (predicted and expected) average crash frequency. Overall, the results of this research support the use of SVM, DT, and RF as predictive tools in network screening." @default.
• W4285384612 created "2022-07-14" @default.
• W4285384612 creator A5002930611 @default.
• W4285384612 creator A5006293608 @default.
• W4285384612 date "2022-09-01" @default.
• W4285384612 modified "2023-10-16" @default.
• W4285384612 title "Validation of Machine Learning Algorithms as Predictive Tool in the Road Safety Management Process: Case of Network Screening" @default.
• W4285384612 cites W113362407 @default.
• W4285384612 cites W1496317909 @default.
• W4285384612 cites W1631393190 @default.
• W4285384612 cites W1973345185 @default.
• W4285384612 cites W1994984228 @default.
• W4285384612 cites W2006868542 @default.
• W4285384612 cites W2010466144 @default.
• W4285384612 cites W2020708932 @default.
• W4285384612 cites W2038175992 @default.
• W4285384612 cites W2066816378 @default.
• W4285384612 cites W2083941252 @default.
• W4285384612 cites W2085349079 @default.
• W4285384612 cites W2105807315 @default.
• W4285384612 cites W2106220766 @default.
• W4285384612 cites W2140485145 @default.
• W4285384612 cites W2150039188 @default.
• W4285384612 cites W2161920802 @default.
• W4285384612 cites W2165137963 @default.
• W4285384612 cites W2170177776 @default.
• W4285384612 cites W2170465906 @default.
• W4285384612 cites W2275318721 @default.
• W4285384612 cites W2292045100 @default.
• W4285384612 cites W303609907 @default.
• W4285384612 doi "https://doi.org/10.1061/jtepbs.0000719" @default.
• W4285384612 hasPublicationYear "2022" @default.
• W4285384612 type Work @default.
• W4285384612 citedByCount "0" @default.
• W4285384612 crossrefType "journal-article" @default.
• W4285384612 hasAuthorship W4285384612A5002930611 @default.
• W4285384612 hasAuthorship W4285384612A5006293608 @default.
• W4285384612 hasConcept C11413529 @default.
• W4285384612 hasConcept C119857082 @default.
• W4285384612 hasConcept C12267149 @default.
• W4285384612 hasConcept C124101348 @default.
• W4285384612 hasConcept C154945302 @default.
• W4285384612 hasConcept C169258074 @default.
• W4285384612 hasConcept C183469790 @default.
• W4285384612 hasConcept C189430467 @default.
• W4285384612 hasConcept C199360897 @default.
• W4285384612 hasConcept C2776436953 @default.
• W4285384612 hasConcept C41008148 @default.
• W4285384612 hasConcept C83209312 @default.
• W4285384612 hasConcept C84525736 @default.
• W4285384612 hasConceptScore W4285384612C11413529 @default.
• W4285384612 hasConceptScore W4285384612C119857082 @default.
• W4285384612 hasConceptScore W4285384612C12267149 @default.
• W4285384612 hasConceptScore W4285384612C124101348 @default.
• W4285384612 hasConceptScore W4285384612C154945302 @default.
• W4285384612 hasConceptScore W4285384612C169258074 @default.
• W4285384612 hasConceptScore W4285384612C183469790 @default.
• W4285384612 hasConceptScore W4285384612C189430467 @default.
• W4285384612 hasConceptScore W4285384612C199360897 @default.
• W4285384612 hasConceptScore W4285384612C2776436953 @default.
• W4285384612 hasConceptScore W4285384612C41008148 @default.
• W4285384612 hasConceptScore W4285384612C83209312 @default.
• W4285384612 hasConceptScore W4285384612C84525736 @default.
• W4285384612 hasIssue "9" @default.
• W4285384612 hasLocation W42853846121 @default.
• W4285384612 hasOpenAccess W4285384612 @default.
• W4285384612 hasPrimaryLocation W42853846121 @default.
• W4285384612 hasRelatedWork W2040826996 @default.
• W4285384612 hasRelatedWork W3081905205 @default.
• W4285384612 hasRelatedWork W375763875 @default.
• W4285384612 hasRelatedWork W4313289487 @default.
• W4285384612 hasRelatedWork W4317732970 @default.
• W4285384612 hasRelatedWork W4321636153 @default.
• W4285384612 hasRelatedWork W4323294312 @default.
• W4285384612 hasRelatedWork W4366990902 @default.
• W4285384612 hasRelatedWork W4386259002 @default.
• W4285384612 hasRelatedWork W626940945 @default.
• W4285384612 hasVolume "148" @default.
• W4285384612 isParatext "false" @default.
• W4285384612 isRetracted "false" @default.
• W4285384612 workType "article" @default.