| Summary: | 碩士 === 國立交通大學 === 資訊科學與工程研究所 === 100 === A modernized transport system usually maintains traffic databases with sufficient historical data. While real-time traffic data can be used to estimate the present traffic states and the short-term traffic forecast, the aggregated historical data actually imply some traffic behaviors by which we can depict the future traffic patterns over a long period and support the on-line traffic information services. In this work, we propose a two-phase mining method to explore the speed patterns given the historical driving data of one road segment. Generally, we estimate the speed patterns on a macroscopic scale in the first phase, and then in the second phase we explore more peak-time patterns on a microscopic scale from their macroscopic appearances. Additionally, the input of our method consists of sequences of time series data recorded over numerous days, and clustering on the sequences is performed based on the similarity measuring of the time series data. Hence, in this work, we analyze the availability of several frequently-used time series similarity measuring methods combined with the clustering methods, and furthermore develop a traffic prediction model with three kinds of predicting functions to examine our two-phase mining method. Finally, in the experiment section, we analyze the performance of our two-phase mining method adopting different selections of similarity measuring method with clustering method, as well as the accuracy of the proposed three prediction functions.
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