THE PREDICTED MODEL OF CAR AND MOTORCYCLE TRAFFIC FLOW AT MIXED TRAFFIC - APPLICATION OF CHAOS ARTIFICIAL NEURAL NETWORK METHOD

• Main Authors: Chin-Feng Hung, 洪啟峰
• Other Authors: Tien-Pen Hsu
• Format: Others
• Language: zh-TW
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/41880961924735472804

碩士 === 國立臺灣大學 === 土木工程學研究所 === 92 === In order to improve road safety, first thing we have to do is to understand the spatiotemporal mixed traffic flow condition for evaluating whether the traffic condition is safe or not. The information is essential for real-time safety control system because it can help real-time safety control system obtain the correct decision. However, mixed traffic flow primarily consists of motorcycle traffic flow and car traffic flow. Therefore, this study will try to discuss and analyze the spatiotemporal changing behavior of mixed flow in detail. First step is to describe mixed flow behavior with chaos theory. Next, the chaos artificial neural network of mixed traffic flow, motorcycle traffic flow and car traffic flow are constructed with neural network theory. In order to ascertain if the prediction ability of the chaos artificial neural network is accurate or not, the mean square error and correlation coefficient are adopted for being the indicator of evaluation. Through the comparative analysis, it is found that chaos phenomenon of mixed traffic flow is more obvious than that of motorcycle traffic flow and car traffic flow. Furthermore, the prediction ability of mixed traffic flow model is more accurate than the other models. It is very essential for forecasting the trend of mixed traffic flow changing condition.