THE PREDICTED MODEL OF CAR AND MOTORCYCLE TRAFFIC FLOW AT MIXED TRAFFIC - APPLICATION OF CHAOS ARTIFICIAL NEURAL NETWORK METHOD
Date Issued
2004
Date
2004
Author(s)
Hung, Chin-Feng
DOI
zh-TW
Abstract
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.
Subjects
渾沌理論
混合車流渾沌預測模式
人工類神經網路
Chaos prediction model of mixed tra
Chaos theory
Type
thesis
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