Comparison between artifical neural network and fuzzy time series to predict road accident / Nur Athirah Mohd Khalil

Road accidents are very common and everyday we always heard a news about people involved in road accidents especially in Malaysia. The statistics of road accidents which come from the website of Jabatan Keselamatan Jalan Raya Malaysia for the year 1975 to 2017 show an increasing number of road accid...

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Main Author:	Mohd Khalil, Nur Athirah
Format:	Thesis
Language:	English
Published:	2019
Subjects:	Traffic accidents Fuzzy arithmetic
Online Access:	http://ir.uitm.edu.my/id/eprint/25260/ http://ir.uitm.edu.my/id/eprint/25260/1/TD_NUR%20ATHIRAH%20MOHD%20KHALIL%20CS%20R%2019_5.pdf

id	uitm-25260
recordtype	eprints
spelling	uitm-252602019-08-26T02:45:12Z http://ir.uitm.edu.my/id/eprint/25260/ Comparison between artifical neural network and fuzzy time series to predict road accident / Nur Athirah Mohd Khalil Mohd Khalil, Nur Athirah Traffic accidents Fuzzy arithmetic Road accidents are very common and everyday we always heard a news about people involved in road accidents especially in Malaysia. The statistics of road accidents which come from the website of Jabatan Keselamatan Jalan Raya Malaysia for the year 1975 to 2017 show an increasing number of road accidents from year to year. This study has been conducted to make a prediction of total road accidents for the future. The secondary data has been used in this study in order to obtain the main objective which is to make a comparison between Artificial Neural Network and Fuzzy Time Series and which method more accurate to make a prediction. Then, this study also contains a sub-objective which is to predict the road accidents in for the next 3 years in Malaysia by using the best method. Furthermore, Artificial Neural Network was generated through Alyuda NeuroIntelligence software by using Quasi-Newton algorithm to get their accuracy. However, the error measure was calculated in this study which is Mean Square Error (MSE) for a comparison which methods gives the lowest value. The result shows that Artificial Neural Network method gives the lowest value of MSE that is 37593818.4 which is more accurate as compared to Fuzzy Time Series is 94186572.8. Thus, the prediction of total road accidents for the next 3 years was produced by using the best method that is Artificial Neural Network. The prediction value for the year 2018, 2019 and 2020 are 522649, 523814 and 524511. 2019-08-21 Thesis NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/25260/1/TD_NUR%20ATHIRAH%20MOHD%20KHALIL%20CS%20R%2019_5.pdf Mohd Khalil, Nur Athirah (2019) Comparison between artifical neural network and fuzzy time series to predict road accident / Nur Athirah Mohd Khalil. Degree thesis, Universiti Teknologi Mara Perlis.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
topic	Traffic accidents Fuzzy arithmetic
spellingShingle	Traffic accidents Fuzzy arithmetic Mohd Khalil, Nur Athirah Comparison between artifical neural network and fuzzy time series to predict road accident / Nur Athirah Mohd Khalil
description	Road accidents are very common and everyday we always heard a news about people involved in road accidents especially in Malaysia. The statistics of road accidents which come from the website of Jabatan Keselamatan Jalan Raya Malaysia for the year 1975 to 2017 show an increasing number of road accidents from year to year. This study has been conducted to make a prediction of total road accidents for the future. The secondary data has been used in this study in order to obtain the main objective which is to make a comparison between Artificial Neural Network and Fuzzy Time Series and which method more accurate to make a prediction. Then, this study also contains a sub-objective which is to predict the road accidents in for the next 3 years in Malaysia by using the best method. Furthermore, Artificial Neural Network was generated through Alyuda NeuroIntelligence software by using Quasi-Newton algorithm to get their accuracy. However, the error measure was calculated in this study which is Mean Square Error (MSE) for a comparison which methods gives the lowest value. The result shows that Artificial Neural Network method gives the lowest value of MSE that is 37593818.4 which is more accurate as compared to Fuzzy Time Series is 94186572.8. Thus, the prediction of total road accidents for the next 3 years was produced by using the best method that is Artificial Neural Network. The prediction value for the year 2018, 2019 and 2020 are 522649, 523814 and 524511.
format	Thesis
author	Mohd Khalil, Nur Athirah
author_facet	Mohd Khalil, Nur Athirah
author_sort	Mohd Khalil, Nur Athirah
title	Comparison between artifical neural network and fuzzy time series to predict road accident / Nur Athirah Mohd Khalil
title_short	Comparison between artifical neural network and fuzzy time series to predict road accident / Nur Athirah Mohd Khalil
title_full	Comparison between artifical neural network and fuzzy time series to predict road accident / Nur Athirah Mohd Khalil
title_fullStr	Comparison between artifical neural network and fuzzy time series to predict road accident / Nur Athirah Mohd Khalil
title_full_unstemmed	Comparison between artifical neural network and fuzzy time series to predict road accident / Nur Athirah Mohd Khalil
title_sort	comparison between artifical neural network and fuzzy time series to predict road accident / nur athirah mohd khalil
publishDate	2019
url	http://ir.uitm.edu.my/id/eprint/25260/ http://ir.uitm.edu.my/id/eprint/25260/1/TD_NUR%20ATHIRAH%20MOHD%20KHALIL%20CS%20R%2019_5.pdf
first_indexed	2023-09-18T23:14:23Z
last_indexed	2023-09-18T23:14:23Z
_version_	1777419014872498176

Comparison between artifical neural network and fuzzy time series to predict road accident / Nur Athirah Mohd Khalil

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