A hybrid clustering and classification approach for predicting crash injury severity on rural roads.
As a threat for transportation system, traffic crashes have a wide range of social consequences for governments. Traffic crashes are increasing in developing countries and Iran as a developing country is not immune from this risk. There are several researches in the literature to predict traffic cra...
| Publicado en: | International Journal of Injury Control & Safety Promotion Vol. 25; no. 1; pp. 85 - 102 |
|---|---|
| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Taylor & Francis Ltd
Mar2018
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=128144025&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 128144025 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17457300 1F4U jtl: International Journal of Injury Control & Safety Promotion issn: 17457300 maglogo: Y pubinfo: dt: Mar2018 vid: 25 iid: 1 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 128144025 128144025 NLM28691578 128144025 10.1080/17457300.2017.1341933 NLM28691578 128144025 ppf: 85 ppct: 17 formats: tig: atl: A hybrid clustering and classification approach for predicting crash injury severity on rural roads. aug: au: Hasheminejad, Seyed Hessam-Allah Zahedi, Mohsen Hasheminejad, Seyed Mohammad Hossein affil: Department of Civil Engineering, Razi University, Kermanshah, Iran sug: subj: Developing Countries Algorithms Models, Statistical Accidents, Traffic Accidents, Traffic Classification Wounds and Injuries Epidemiology Cluster Analysis Child Iran Male Adult Young Adult Female Child, Preschool Middle Age Trauma Severity Indices Forecasting Infant Aged Environment Adolescence Rural Population Infant, Newborn Human Neural Networks (Computer) Validation Studies Comparative Studies Evaluation Research Multicenter Studies Child: 6-12 years Adult: 19-44 years Child, Preschool: 2-5 years Middle Aged: 45-64 years Infant: 1-23 months Aged: 65+ years Adolescent: 13-18 years Infant, Newborn: birth-1 month Male Female ab: As a threat for transportation system, traffic crashes have a wide range of social consequences for governments. Traffic crashes are increasing in developing countries and Iran as a developing country is not immune from this risk. There are several researches in the literature to predict traffic crash severity based on artificial neural networks (ANNs), support vector machines and decision trees. This paper attempts to investigate the crash injury severity of rural roads by using a hybrid clustering and classification approach to compare the performance of classification algorithms before and after applying the clustering. In this paper, a novel rule-based genetic algorithm (GA) is proposed to predict crash injury severity, which is evaluated by performance criteria in comparison with classification algorithms like ANN. The results obtained from analysis of 13,673 crashes (5600 property damage, 778 fatal crashes, 4690 slight injuries and 2605 severe injuries) on rural roads in Tehran Province of Iran during 2011–2013 revealed that the proposed GA method outperforms other classification algorithms based on classification metrics like precision (86%), recall (88%) and accuracy (87%). Moreover, the proposed GA method has the highest level of interpretation, is easy to understand and provides feedback to analysts. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|