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Type :thesis
Subject :HE Transportation and Communications
Main Author :Noor Wahida Md Junus
Title :Modeling Malaysian road accidents:the structural time series approach
Place of Production :Tanjong Malim
Publisher :Fakulti Sains dan Matematik
Year of Publication :2018
Corporate Name :Universiti Pendidikan Sultan Idris
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Abstract : Universiti Pendidikan Sultan Idris
Modeling the number of road accidents occurrence is a quite common topic in recent years. A number of studies have been developed with the aim to find the best model that gives better prediction. However, statistical patterns such as trend and seasonality of road accidents is rarely observed. Estimating the pattern of trend and seasonal will indirectly provide a better impact on prediction system. Traditionally, estimation of trend and seasonal patterns are made based on decomposition method. Yet, this type of estimation shows intangible predictions as the estimation are based on deterministic form. Therefore, structural time series (STS) approach is proposed to model the trend and seasonal pattern of road accidents occurrence. The STS approach offered a direct interpretation and allowed the time series component including trend and seasonal to vary over time. In this thesis the road accidents model is developed using the STS approach with the aim to observe the pattern of trend and seasonality of road accidents occurrence. This thesis was done on all 5 main regions and 14 states in Malaysia. The study further enhance investigation on road accidents influences at different locations with appropriate explanatory variables. There are 8 explanatory variables considered in this study, which includes four climate variables, two economic variables, seasonal related variable and safety related variable. Effectiveness of the model is measured by comparing 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 occurrence vary in different locations. The number of accidents was estimated to be higher during festival seasons especially in non-developing states. Besides, the special features of the stochastic behavior of road accidents pattern is also observed. During the study period, the pattern of road accidents is fluctuate between increasing and decreasing. Similarly, the influence of road accidents in different locations also varies. In terms of the prediction and forecasting performance, STS gave more reliable prediction and forecasting compared to TSR and SARIMA models.


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