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首页> 美国政府科技报告 >Freeway Traffic Data Prediction Using Artificial Neural Networks and Developmentof a Fuzzy Logic Ramp Metering Algorithm
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Freeway Traffic Data Prediction Using Artificial Neural Networks and Developmentof a Fuzzy Logic Ramp Metering Algorithm

机译：基于人工神经网络的高速公路交通数据预测及模糊逻辑斜坡计量算法的研究

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This research project develops a fuzzy logic ramp metering algorithm utilizingartificial neural network (ANN) traffic data predictors. Considering the highly beneficial effects of ramp metering, such as reduced travel times and lower accident rates, optimizing metering rates is of great importance. The research objective is to overcome limitations of the current Seattle ramp metering algorithm, which reacts to existing bottlenecks rather than preventing them. An algorithm with predictive capabilities can help prevent or delay bottleneck formation. Hence, an accurate 1-minute ANN prediction provides a powerful asset to the ramp metering algorithm. The research project divides into two stages: the ANN traffic data predictor and the fuzzy logic ramp metering algorithms. This research focuses primarily on the ANN traffic data predictors, but also lays the groundwork for the fuzzy logic ramp metering concepts and algorithm.

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