Accident Chain and Causality Analysis
|關鍵字:||事故鏈;交通安全;粗略集合;總計誤差;異質性;Accident Chain;Traffic Safety;Rough Sets;Aggregation Bias;Heterogeneity|
Analyzing accident causality has been one of the many ways to enhance traffic safety. The objective of this research was to explore contributing factors and accident causality by utilizing crash databases with mature methodologies and powerful computational powers from chain perspective. Rough sets theory was adopted in this research to obtain accident chains from cross-sectional databases. This theory is advantageous due to its ability to simultaneously control numerous factors, which reflect the fact that the occurrence of accidents results from complex interactions of many contributing factors. The other advantage is that rough set rules are generated by comparing the individual differences, which would partially alleviate the issue of aggregation bias. Three studies were conducted based on the concept of accident chains. The first study was to assess the ability of rough sets theory in explaining the underlying process of accident occurrence and in demonstrating accident chains by systematically loading combinations of condition attributes into rough sets. Second, the issue of data heterogeneity was examined from chain perspective by grouping accidents with the occurring frequency of rules. Finally, accident causality was addressed by comparing individual rules in pairs. Taiwan's crash databases were adopted in the empirical study, where single auto-vehicle (SAV) accidents were chosen as the subject to analysis. It was found that lower/upper approximation, accuracy of approximation, quality of approximation, number of generated rules, and hit rates could effectively address the differences between accident types. The occurrence of crashes with facility may follow similar paths and is more predictable; these crashes have some similarities between the crashes with architecture, with facility, off-road and rollover types. Moreover, significantly different features were shown between frequently repeated and sparsely unique rules. The former rules linked to the characteristics of high-risk drivers shown in past studies while the latter was connected with poor road conditions. Providing better road environment has been considered as an effective way to improve traffic safety; however, better roads could encourage high-risk drivers to raise their driving speeds. Furthermore, instead of one single factor the combinations of unfavorable factors were found to be the causes leading to fatal accidents. If one or several undesired factors were removed from the chain, accident severity might be reduced. The proposed approaches in the research provide a way to analyze accidents closer to the essence of accident occurrence. Meanwhile, these approaches also provide alternative ways to alleviate issues often seen in safety research such as aggregation bias, heterogeneity of accident data, confounding factors, and so on. These approaches can be expanded based on analysts' on-hand data and their understanding of target subjects.
|Appears in Collections:||Thesis|
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