Accident prediction model at un-signalized intersections using multiple regression method

Nowadays, accident increased relatively from year to year although many programs have been carried out by the authority in order to reduce the number of accident. In Johor areas, seventeen accident hotspots have been identified in the state. The road accident increase proportionate to growth i...

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Main Author:	Wan Manan, Wan Nadiah
Format:	Thesis
Language:	English English English
Published:	2011
Subjects:	HE Transportation and Communications
Online Access:	http://eprints.uthm.edu.my/2868/1/24p%20WAN%20NADIAH%20WAN%20MANAN.pdf http://eprints.uthm.edu.my/2868/2/WAN%20NADIAH%20WAN%20MANAN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/2868/3/WAN%20NADIAH%20WAN%20MANAN%20WATERMARK.pdf
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id	my-uthm-ep.2868
record_format	uketd_dc
spelling	my-uthm-ep.28682021-11-02T03:29:35Z Accident prediction model at un-signalized intersections using multiple regression method 2011-05 Wan Manan, Wan Nadiah HE Transportation and Communications HE5601-5725 Automotive transportation Including trucking, bus lines, and taxicab service Nowadays, accident increased relatively from year to year although many programs have been carried out by the authority in order to reduce the number of accident. In Johor areas, seventeen accident hotspots have been identified in the state. The road accident increase proportionate to growth in population, economic in development, industrialization and motorization that encountered by the country. The roadway geometric and traffic condition are among important factors in causes to traffic accidents. Field work is carried out to collect data such as traffic volume, mean speed of vehicles, lane width, shoulder width, lane used, number of intersection and also number legs intersection at the selected locations. Metrocount and odometer were used for this purpose. By considering the factors that contribute to the accident, this study was carried out to develop the accident prediction model using Multiple Regression approach. Accident prediction models are invaluable tools that have many applications in road safety analysis. In accident analysis, statistical models have been used in highway and traffic safety studies. From the results shows that accident point weigtage can be explained by increase of traffic volume and vehicle speed in Federal Route 001 and Federal Route 024 are the contributors to traffic accidents. Meanwhile, an increment of lane width and shoulder width will reduce the weighting point rates Finally, the Accident Prediction Model developed in this study not only can be used to reduce the number of accidents in the future but also for intersection treatment or upgrading. Using the model, appropriate design parameters of un-signalized intersection could be specified. 2011-05 Thesis http://eprints.uthm.edu.my/2868/ http://eprints.uthm.edu.my/2868/1/24p%20WAN%20NADIAH%20WAN%20MANAN.pdf text en public http://eprints.uthm.edu.my/2868/2/WAN%20NADIAH%20WAN%20MANAN%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/2868/3/WAN%20NADIAH%20WAN%20MANAN%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Fakulti Kejuruteraan Awam dan Alam Bina
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	HE Transportation and Communications HE Transportation and Communications
spellingShingle	HE Transportation and Communications HE Transportation and Communications Wan Manan, Wan Nadiah Accident prediction model at un-signalized intersections using multiple regression method
description	Nowadays, accident increased relatively from year to year although many programs have been carried out by the authority in order to reduce the number of accident. In Johor areas, seventeen accident hotspots have been identified in the state. The road accident increase proportionate to growth in population, economic in development, industrialization and motorization that encountered by the country. The roadway geometric and traffic condition are among important factors in causes to traffic accidents. Field work is carried out to collect data such as traffic volume, mean speed of vehicles, lane width, shoulder width, lane used, number of intersection and also number legs intersection at the selected locations. Metrocount and odometer were used for this purpose. By considering the factors that contribute to the accident, this study was carried out to develop the accident prediction model using Multiple Regression approach. Accident prediction models are invaluable tools that have many applications in road safety analysis. In accident analysis, statistical models have been used in highway and traffic safety studies. From the results shows that accident point weigtage can be explained by increase of traffic volume and vehicle speed in Federal Route 001 and Federal Route 024 are the contributors to traffic accidents. Meanwhile, an increment of lane width and shoulder width will reduce the weighting point rates Finally, the Accident Prediction Model developed in this study not only can be used to reduce the number of accidents in the future but also for intersection treatment or upgrading. Using the model, appropriate design parameters of un-signalized intersection could be specified.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Wan Manan, Wan Nadiah
author_facet	Wan Manan, Wan Nadiah
author_sort	Wan Manan, Wan Nadiah
title	Accident prediction model at un-signalized intersections using multiple regression method
title_short	Accident prediction model at un-signalized intersections using multiple regression method
title_full	Accident prediction model at un-signalized intersections using multiple regression method
title_fullStr	Accident prediction model at un-signalized intersections using multiple regression method
title_full_unstemmed	Accident prediction model at un-signalized intersections using multiple regression method
title_sort	accident prediction model at un-signalized intersections using multiple regression method
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Kejuruteraan Awam dan Alam Bina
publishDate	2011
url	http://eprints.uthm.edu.my/2868/1/24p%20WAN%20NADIAH%20WAN%20MANAN.pdf http://eprints.uthm.edu.my/2868/2/WAN%20NADIAH%20WAN%20MANAN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/2868/3/WAN%20NADIAH%20WAN%20MANAN%20WATERMARK.pdf
_version_	1747830996853587968

Accident prediction model at un-signalized intersections using multiple regression method

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