Applying Bayesian Models to Analyze Motorcycle Crashes at Signalized Intersections by Collision Types

• Main Authors: Yu Yen, 顏愉
• Other Authors: Tien-Pen Hsu
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/81763885410742482808

碩士 === 國立臺灣大學 === 土木工程學研究所 === 103 === Since the motorcycle is the most common type of vehicle used in Taiwan, it accounts for the largest proportion of all of Taiwan’s vehicles. Consequently, it is important to accurately understand the frequency of motorcycle crashs. However, the mixed traffic characterists (including motorcycles and cars) on Taiwan’s roads increses the difficulty of modeling such crashes. The objective of this study is to identify the impact factors of crashes at signalized intersections. This study applied Bayesian models in an effort to analyze motorcycle crashes according to the following common collision types that occur at signal-controlled intersection: through with right turn, through with opposing left turn, right angle, and sideswipe from the same direction. A total of 128 approaches to 32 four-leg intersections in Taiwan were used for this analysis. Two Bayesian models, the Hierarchical Poisson-gamma and Poisson-lognormal models, were explored. Compared to DIC, the Hierarchical Poisson-gamma model was found to be better than the Poisson-lognormal model for all four collision types. The results of this study show the relationship between the crash frequency of motorcycles and the intersection characteristics that occur in mixed-traffic flows, which is representative of the unique road designs and traffic control methods common in Taiwan (including the presence of express / slow traffic dividers, the number of motorcycle permit lanes, and the number of fast lanes). The results indicate that different collision types can be attributed to different accident factors. The results also show that an accident factor analysis performed according to collision type can be useful in describing the targeted causes. It is expected that the results of this study will help to develop more effective corresponding safety countermeasures in areas of Southeast Asia faced with such mixed traffic flows, such as Taiwan, China, and Viennam.