Road safety performance index: A tool for crash prediction. Issue 1 (31st December 2022)
- Record Type:
- Journal Article
- Title:
- Road safety performance index: A tool for crash prediction. Issue 1 (31st December 2022)
- Main Title:
- Road safety performance index: A tool for crash prediction
- Authors:
- Shbeeb, Lina
- Abstract:
- Abstract: The relationship between road infrastructure parameters and road crashes is not widely investigated in developing countries. The study objective is to create a measure for rating road safety performance as a tool to predict road crashes. A checklist included 184 elements categorized into nine groups, a sample of 105 selected streets in two districts with different population densities and incomes in Amman. The road elements' safety impact was rated by two groups (professionals and non-professionals). Other data include vehicular and pedestrian traffic, speed, and road crash data. The principal component analysis was used to reduce variables and generalized linear models for the crash prediction considered the parameters or the road safety performance index as the predictors (un/extracted). The results showed that the road safety performance index in the less dense area is better than that of the densely populated. The professionals' and non-professionals' safety ratings did not establish a significant consensus. Modeling results indicated that using unextracted predictors poorly predicts road crashes for both the parameters and the Road-Safety-Performance-Index (RSPI). The safety-weighted predictors used in the modeling provide a valid estimate for road crashes with a high correlation of 69% for the) RSPI. Additional traffic parameters improve, to some extent, the prediction power of the RSPI-based models. The study concluded with the need to develop policies andAbstract: The relationship between road infrastructure parameters and road crashes is not widely investigated in developing countries. The study objective is to create a measure for rating road safety performance as a tool to predict road crashes. A checklist included 184 elements categorized into nine groups, a sample of 105 selected streets in two districts with different population densities and incomes in Amman. The road elements' safety impact was rated by two groups (professionals and non-professionals). Other data include vehicular and pedestrian traffic, speed, and road crash data. The principal component analysis was used to reduce variables and generalized linear models for the crash prediction considered the parameters or the road safety performance index as the predictors (un/extracted). The results showed that the road safety performance index in the less dense area is better than that of the densely populated. The professionals' and non-professionals' safety ratings did not establish a significant consensus. Modeling results indicated that using unextracted predictors poorly predicts road crashes for both the parameters and the Road-Safety-Performance-Index (RSPI). The safety-weighted predictors used in the modeling provide a valid estimate for road crashes with a high correlation of 69% for the) RSPI. Additional traffic parameters improve, to some extent, the prediction power of the RSPI-based models. The study concluded with the need to develop policies and procedures to improve road conditions and their performance, thus enhancing road safety. … (more)
- Is Part Of:
- Cogent engineering. Volume 9:Issue 1(2022)
- Journal:
- Cogent engineering
- Issue:
- Volume 9:Issue 1(2022)
- Issue Display:
- Volume 9, Issue 1 (2022)
- Year:
- 2022
- Volume:
- 9
- Issue:
- 1
- Issue Sort Value:
- 2022-0009-0001-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-12-31
- Subjects:
- crash prediction -- general linear models -- perfomance index -- principal component analysis -- road parameters parameters -- road safety
Engineering -- Periodicals
Technology -- Periodicals
Engineering
Technology
Periodicals
620 - Journal URLs:
- http://bibpurl.oclc.org/web/73324 ↗
http://cogentoa.tandfonline.com/journal/oaen20 ↗
http://www.tandfonline.com/toc/oaen20/1/1 ↗
http://www.tandfonline.com/ ↗
http://cogentoa.tandfonline.com/journal/oaps20 ↗ - DOI:
- 10.1080/23311916.2022.2124637 ↗
- Languages:
- English
- ISSNs:
- 2331-1916
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 24494.xml