UPSI Digital Repository (UDRep)
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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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