Development of accident prediction model by using artificial neural network (ANN)

Statistical or crash prediction model have frequently been used in highway safety studies. They can be used in identify major contributing factors or establish relationship between crashes and explanatory accident variables. The measurements to prevent accident are from the speed reduction, wide...

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Main Author:	Ramli, Mohd Zakwan
Format:	Thesis
Language:	English English English
Published:	2011
Subjects:	HE Transportation and Communications
Online Access:	http://eprints.uthm.edu.my/2728/1/24p%20MOHD%20ZAKWAN%20RAMLI.pdf http://eprints.uthm.edu.my/2728/2/MOHD%20ZAKWAN%20RAMLI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/2728/3/MOHD%20ZAKWAN%20RAMLI%20WATERMARK.pdf
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id	my-uthm-ep.2728
record_format	uketd_dc
spelling	my-uthm-ep.27282021-11-01T06:16:21Z Development of accident prediction model by using artificial neural network (ANN) 2011-05 Ramli, Mohd Zakwan HE Transportation and Communications HE5601-5725 Automotive transportation Including trucking, bus lines, and taxicab service Statistical or crash prediction model have frequently been used in highway safety studies. They can be used in identify major contributing factors or establish relationship between crashes and explanatory accident variables. The measurements to prevent accident are from the speed reduction, widening the roads, speed enforcement, or construct the road divider, or other else. Therefore, the purpose of this study is to develop an accident prediction model at federal road FT 050 Batu Pahat to Kluang. The study process involves the identification of accident blackspot locations, establishment of general patterns of accident, analysis of the factors involved, site studies, and development of accident prediction model using Artificial Neural Network (ANN) applied software which named NeuroShell2. The significant of the variables that are selected from these accident factors are checked to ensure the developed model can give a good prediction results. The performance of neural network is evaluated by using the Mean Absolute Percentage Error (MAPE). The study result showed that the best neural network for accident prediction model at federal road FT 050 is 4-10-1 with 0.1 learning rate and 0.2 momentum rate. This network model contains the lowest value of MAPE and highest value of linear correlation, r which is 0.8986. This study has established the accident point weightage as the rank of the blackspot section by kilometer along the FT 050 road (km 1 – km 103). Several main accident factors also have been determined along this road, and after all the data gained, it has successfully analyzed by using artificial neural network. 2011-05 Thesis http://eprints.uthm.edu.my/2728/ http://eprints.uthm.edu.my/2728/1/24p%20MOHD%20ZAKWAN%20RAMLI.pdf text en public http://eprints.uthm.edu.my/2728/2/MOHD%20ZAKWAN%20RAMLI%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/2728/3/MOHD%20ZAKWAN%20RAMLI%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein 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 Ramli, Mohd Zakwan Development of accident prediction model by using artificial neural network (ANN)
description	Statistical or crash prediction model have frequently been used in highway safety studies. They can be used in identify major contributing factors or establish relationship between crashes and explanatory accident variables. The measurements to prevent accident are from the speed reduction, widening the roads, speed enforcement, or construct the road divider, or other else. Therefore, the purpose of this study is to develop an accident prediction model at federal road FT 050 Batu Pahat to Kluang. The study process involves the identification of accident blackspot locations, establishment of general patterns of accident, analysis of the factors involved, site studies, and development of accident prediction model using Artificial Neural Network (ANN) applied software which named NeuroShell2. The significant of the variables that are selected from these accident factors are checked to ensure the developed model can give a good prediction results. The performance of neural network is evaluated by using the Mean Absolute Percentage Error (MAPE). The study result showed that the best neural network for accident prediction model at federal road FT 050 is 4-10-1 with 0.1 learning rate and 0.2 momentum rate. This network model contains the lowest value of MAPE and highest value of linear correlation, r which is 0.8986. This study has established the accident point weightage as the rank of the blackspot section by kilometer along the FT 050 road (km 1 – km 103). Several main accident factors also have been determined along this road, and after all the data gained, it has successfully analyzed by using artificial neural network.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Ramli, Mohd Zakwan
author_facet	Ramli, Mohd Zakwan
author_sort	Ramli, Mohd Zakwan
title	Development of accident prediction model by using artificial neural network (ANN)
title_short	Development of accident prediction model by using artificial neural network (ANN)
title_full	Development of accident prediction model by using artificial neural network (ANN)
title_fullStr	Development of accident prediction model by using artificial neural network (ANN)
title_full_unstemmed	Development of accident prediction model by using artificial neural network (ANN)
title_sort	development of accident prediction model by using artificial neural network (ann)
granting_institution	Universiti Tun Hussein Malaysia
granting_department	Fakulti Kejuruteraan Awam dan Alam Bina
publishDate	2011
url	http://eprints.uthm.edu.my/2728/1/24p%20MOHD%20ZAKWAN%20RAMLI.pdf http://eprints.uthm.edu.my/2728/2/MOHD%20ZAKWAN%20RAMLI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/2728/3/MOHD%20ZAKWAN%20RAMLI%20WATERMARK.pdf
_version_	1747830980519919616

Development of accident prediction model by using artificial neural network (ANN)

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