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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| Format: | thesis |
| Language: | eng |
| Published: |
2018
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| Subjects: | |
| Online Access: | https://ir.upsi.edu.my/detailsg.php?det=5248 |
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| Summary: | 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. |
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