การสืบค้นสาเหตุของอุบัติเหตุทางถนนในเชิงลึกโดยใช้การวิเคราะห์ต้นไม้แห่งความล้มเหลวแบบฟัซซี่

Abstract

Road accidents are one of the most pressing issues, which lead to the loss of economic, social, and quality of life. A road traffic accident occurrence can be caused by one or many causal factors; therefore, it is necessary to conduct an accident investigation to identify the basic causes of such accidents. This study focuses on the analytical method for in-depth road accident investigations by using the Fault Tree Analysis (FTA) method. This technique helps explain the relationships among different causal factors in a systematic manner. The analysis process starts from selecting the accident type as a top event of the tree structure to analyzing the factors affecting the accident occurrence the most by considering removing most common causes of accidents individually. This traditional FTA method requires the accident statistics and data as inputs of the analysis; however, such data are limited and incomplete. This study proposes applying the fuzzy set theory to explain the significance of each causal factor derived from expert opinions in terms of the probability that affects an accident. The application determines the causes of accidents and recommend corrective actions to improve road safety. This research studies 5 difference accident cases. The first case is school bus was hit at the back skirt by a 10-wheel truck, the second case is a 6-wheel truck was crashed by a bus, the third case is an overload on motorcycle with 3 people lost it balance on a road improvement zone, the fourth is a taxi crashed by a 4-wheel truck fell from elevated highway and the fifth case is a double-decker bus fell off the road. The results of this study present in two parts; (i) investigating the cause of the accident; (ii) determining the probability of the cause of the accident. In the first past, this study can point out that most of the accidents caused by human behavior (e.g., driving at overspeed, sleepy while driving, Error in gap judgment, Inattentive driving) vehicle factor (e.g., modified tire, diecasts alloy wheels, tire rubber, Braking system failure, modified car) and environment factor (e.g., poor warning of the road junction, poor visions and difference at shoulder and pavement level.) In the second part found that the most accident factor caused by human by 60.03%, vehicle by 7.89% and environment by 32.08%.