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...

Full description

Bibliographic Details
Main Author: Wan Manan, Wan Nadiah
Format: Thesis
Language:English
English
English
Published: 2011
Subjects:
Online Access:http://eprints.uthm.edu.my/2868/
Abstract Abstract here
_version_ 1855520565981347840
author Wan Manan, Wan Nadiah
author_facet Wan Manan, Wan Nadiah
author_sort Wan Manan, Wan Nadiah
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
id uthm-2868
institution Universiti Tun Hussein Onn Malaysia
language English
English
English
publishDate 2011
record_format EPrints
record_pdf Restricted
spelling uthm-28682021-11-02T03:29:35Z http://eprints.uthm.edu.my/2868/ Accident prediction model at un-signalized intersections using multiple regression method 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 NonPeerReviewed text en http://eprints.uthm.edu.my/2868/1/24p%20WAN%20NADIAH%20WAN%20MANAN.pdf text en http://eprints.uthm.edu.my/2868/2/WAN%20NADIAH%20WAN%20MANAN%20COPYRIGHT%20DECLARATION.pdf text en http://eprints.uthm.edu.my/2868/3/WAN%20NADIAH%20WAN%20MANAN%20WATERMARK.pdf Wan Manan, Wan Nadiah (2011) Accident prediction model at un-signalized intersections using multiple regression method. Masters thesis, Universiti Tun Hussein Onn Malaysia.
spellingShingle HE Transportation and Communications
HE5601-5725 Automotive transportation Including trucking, bus lines, and taxicab service
Wan Manan, Wan Nadiah
Accident prediction model at un-signalized intersections using multiple regression method
thesis_level Master
title 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_short Accident prediction model at un-signalized intersections using multiple regression method
title_sort accident prediction model at un signalized intersections using multiple regression method
topic HE Transportation and Communications
HE5601-5725 Automotive transportation Including trucking, bus lines, and taxicab service
url http://eprints.uthm.edu.my/2868/
work_keys_str_mv AT wanmananwannadiah accidentpredictionmodelatunsignalizedintersectionsusingmultipleregressionmethod