A study on knowledge acquisition from a large-scale traffic data set by using structured data mining techniques

本研究的目的是应用结构数据挖掘技术的信息和通信系统记录的大规模交通数据，以获得信息，有助于交通规划和建立分析技术，自动提取特征运动模式。在这项研究中，我们试图应用各种数据挖掘技术的人的行程数据，这是统计数据描述的人在城市中的运动和日志数据的社区自行车（自行车共享系统在城市地区），这是一种新的交通方式在城市。其结果是，能够提取用于将特征移动模式的估计修正为大规模数据集的知识，能够构建高精度的交通选择模型，能够了解日常交通和非日常交通时的交通模式的变化等整个交通现象。

The aims of this study are to apply the structure data mining techniques for large-scale traffic data recorded by an information and communication system to obtain information to contribute to a transportation planning and to build analysis technique to extract a characteristic movement pattern automatically. In this study, we tried to apply various data mining techniques for the person trip data which were the statistics data described the movement of the person in the city and log data of community cycle (bicycle sharing system in an urban area) which was the new transportation mode in the city. As a result, the extraction of the knowledge to modify the estimate of a characteristic movement pattern to be in large-scale data set, the construction of a highly precise transportation choice model, the whole traffic phenomenon such as the change in the traffic pattern at the daily traffic and the non-daily traffic was enabled.