| Summary: | 碩士 === 國立中正大學 === 資訊工程研究所 === 104 === With the increase in population, the number of vehicles on the road has increased rapidly, the traffic issues are becoming the focus of attention. Therefore, Intelligent Transportation Systems (ITS) were proposed to improve the traffic problems and increase the transport efficiency. There is a subsystem of ITS called Advanced Traveler Information Systems (ATIS). ATIS depend on advanced information technology and communication technology to provide the traffic information to drivers in real time. The traffic information can be the references of route choices.
In this Thesis, we proposed an Urban Traffic Information Prediction Systems (UTIPS). This system contains traffic data and network data pre-processing method and traffic information prediction model, which we use Elman Neural Network to be the prediction model. We adopt the open data to be our historical traffic data, and the historical traffic data is used to predict future traffic information with above method and model. In addition, in the training and prediction process of prediction model, we can consider the traffic information of upstream sections to improve the accuracy of prediction results.
Experiments show that the Mean Absolute Percentage Error (MAPE) of our traffic volumes prediction considering the traffic information of upstream section is less than 10%, and the MAPE of traffic volumes prediction without considering that of upstream section is also less than 12%.
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