Modeling Malaysian road accidents:the structural time series approach

Modeling the number of road accidents occurrence is a quite common topicin recent years. A number of studies have been developed with the aim to find thebest model that gives better prediction. However, statistical patterns such as trendand seasonality of road accidents is rarely observed. Estimatin...

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Main Author:	Noor Wahida Md Junus
Format:	thesis
Language:	eng
Published:	2018
Subjects:	HE Transportation and Communications
Online Access:	https://ir.upsi.edu.my/detailsg.php?det=5248
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institution	Universiti Pendidikan Sultan Idris
collection	UPSI Digital Repository
language	eng
topic	HE Transportation and Communications
spellingShingle	HE Transportation and Communications Noor Wahida Md Junus Modeling Malaysian road accidents:the structural time series approach
description	Modeling the number of road accidents occurrence is a quite common topicin recent years. A number of studies have been developed with the aim to find thebest model that gives better prediction. However, statistical patterns such as trendand seasonality of road accidents is rarely observed. Estimating the pattern of trendand seasonal will indirectly provide a better impact on prediction system.Traditionally, estimation of trend and seasonal patterns are made based ondecomposition method. Yet, this type of estimation shows intangible predictions asthe estimation are based on deterministic form. Therefore, structural time series(STS) approach is proposed to model the trend and seasonal pattern of road accidentsoccurrence. The STS approach offered a direct interpretation and allowed the timeseries component including trend and seasonal to vary over time. In this thesis theroad accidents model is developed using the STS approach with the aim to observethe pattern of trend and seasonality of road accidents occurrence. This thesis wasdone on all 5 main regions and 14 states in Malaysia. The study further enhanceinvestigation on road accidents influences at different locations with appropriateexplanatory variables. There are 8 explanatory variables considered in this study,which includes four climate variables, two economic variables, seasonal relatedvariable and safety related variable. Effectiveness of the model is measured bycomparing their prediction and forecasting performance with time series regression(TSR) and seasonal autoregressive integrated moving average (SARIMA) models.The study found that the trend and seasonal patterns of road accidents occurrencevary in different locations. The number of accidents was estimated to be higherduring festival seasons especially in non-developing states. Besides, the specialfeatures of the stochastic behavior of road accidents pattern is also observed. Duringthe study period, the pattern of road accidents is fluctuate between increasing anddecreasing. Similarly, the influence of road accidents in different locations alsovaries. In terms of the prediction and forecasting performance, STS gave morereliable prediction and forecasting compared to TSR and SARIMA models.
format	thesis
qualification_name
qualification_level	Doctorate
author	Noor Wahida Md Junus
author_facet	Noor Wahida Md Junus
author_sort	Noor Wahida Md Junus
title	Modeling Malaysian road accidents:the structural time series approach
title_short	Modeling Malaysian road accidents:the structural time series approach
title_full	Modeling Malaysian road accidents:the structural time series approach
title_fullStr	Modeling Malaysian road accidents:the structural time series approach
title_full_unstemmed	Modeling Malaysian road accidents:the structural time series approach
title_sort	modeling malaysian road accidents:the structural time series approach
granting_institution	Universiti Pendidikan Sultan Idris
granting_department	Fakulti Sains dan Matematik
publishDate	2018
url	https://ir.upsi.edu.my/detailsg.php?det=5248
_version_	1747833175534469120
spelling	oai:ir.upsi.edu.my:52482020-09-21 Modeling Malaysian road accidents:the structural time series approach 2018 Noor Wahida Md Junus HE Transportation and Communications Modeling the number of road accidents occurrence is a quite common topicin recent years. A number of studies have been developed with the aim to find thebest model that gives better prediction. However, statistical patterns such as trendand seasonality of road accidents is rarely observed. Estimating the pattern of trendand seasonal will indirectly provide a better impact on prediction system.Traditionally, estimation of trend and seasonal patterns are made based ondecomposition method. Yet, this type of estimation shows intangible predictions asthe estimation are based on deterministic form. Therefore, structural time series(STS) approach is proposed to model the trend and seasonal pattern of road accidentsoccurrence. The STS approach offered a direct interpretation and allowed the timeseries component including trend and seasonal to vary over time. In this thesis theroad accidents model is developed using the STS approach with the aim to observethe pattern of trend and seasonality of road accidents occurrence. This thesis wasdone on all 5 main regions and 14 states in Malaysia. The study further enhanceinvestigation on road accidents influences at different locations with appropriateexplanatory variables. There are 8 explanatory variables considered in this study,which includes four climate variables, two economic variables, seasonal relatedvariable and safety related variable. Effectiveness of the model is measured bycomparing their prediction and forecasting performance with time series regression(TSR) and seasonal autoregressive integrated moving average (SARIMA) models.The study found that the trend and seasonal patterns of road accidents occurrencevary in different locations. The number of accidents was estimated to be higherduring festival seasons especially in non-developing states. Besides, the specialfeatures of the stochastic behavior of road accidents pattern is also observed. Duringthe study period, the pattern of road accidents is fluctuate between increasing anddecreasing. Similarly, the influence of road accidents in different locations alsovaries. In terms of the prediction and forecasting performance, STS gave morereliable prediction and forecasting compared to TSR and SARIMA models. 2018 thesis https://ir.upsi.edu.my/detailsg.php?det=5248 https://ir.upsi.edu.my/detailsg.php?det=5248 text eng closedAccess Doctoral Universiti Pendidikan Sultan Idris Fakulti Sains dan Matematik Abdul Manan, M. M., ?onsson, T., & Varhelyi, A. (2013). Development of a safetyperformance function for motorcycle accident fatalities on Malaysian primaryroads. Safety Science, 60, 13-20. https:lldoi.org/10.l016/j.ssci.2013.06.005Abdul Manan, M. M., & Varhelyi, A. (2012). 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