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Study of Road Accident Prediction Model by Using Pls-Sem

Interim Semester 2020/2021

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Main Author: Nur Fadilah binti Adriyanshah
Format: text::Thesis
Published: 2023
Subjects:
Rate of Accidents
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spelling my.uniten.dspace-216922023-05-04T13:38:18Z Study of Road Accident Prediction Model by Using Pls-Sem Nur Fadilah binti Adriyanshah Rate of Accidents Interim Semester 2020/2021 Many developed countries in line with the increase in road transport, and consequently an increase in the rate of accidents, are searching for effective ways to reduce road accidents including Malaysia. In the area of traffic safety, in order to identify factors contributing to accidents, conventional methods which generally based on regression analysis are used. However, these methods only detect accidents in different roads, but cannot clearly identify the cause of accidents and define the relationship between them. In addition, the methods used have two major limitations: 1- Postulate the structure of the model, and, 2- Observability of all variables. Due to the limitations discussed and also due to the complex nature of human factors, and the impact of road conditions, vehicle and environment on human factors, the aim of this study is to provide a useful tool for defining and measuring road, traffic and human factors, to evaluate the effect of each of them in accidents which caused by carelessness, directly and indirectly by using structural equation modeling with the partial least squares approach. Compared with the regression-based techniques or methods of pattern recognition that only a layer of relationships between independent and dependent variables is determined, the SEM approach provides the possibility of modeling the relationships between multiple independent and dependent structures. Moreover, the ability to use unobservable hidden variables, by using observable variables would be possible. This study also explained briefly current trends at FT050 base on traffic engineering observation and succeed to identify and rank factors influencing road accident at FT050. Data used for this modelling are based on three main data which are questionnaire data for human behaviour factor , traffic study data where onsite investigation has been done and lastly accident data taken from Ministry of Works Malaysia for road and surrounding factor. 2023-05-03T17:41:19Z 2023-05-03T17:41:19Z 2020-09 Resource Types::text::Thesis https://irepository.uniten.edu.my/handle/123456789/21692 application/pdf
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Rate of Accidents
spellingShingle Rate of Accidents
Nur Fadilah binti Adriyanshah
Study of Road Accident Prediction Model by Using Pls-Sem
description Interim Semester 2020/2021
format Resource Types::text::Thesis
author Nur Fadilah binti Adriyanshah
author_facet Nur Fadilah binti Adriyanshah
author_sort Nur Fadilah binti Adriyanshah
title Study of Road Accident Prediction Model by Using Pls-Sem
title_short Study of Road Accident Prediction Model by Using Pls-Sem
title_full Study of Road Accident Prediction Model by Using Pls-Sem
title_fullStr Study of Road Accident Prediction Model by Using Pls-Sem
title_full_unstemmed Study of Road Accident Prediction Model by Using Pls-Sem
title_sort study of road accident prediction model by using pls-sem
publishDate 2023
_version_ 1806423993533595648
score 13.214268

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