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浮动车信息采集得到越来越广泛的应用. 由于交通流数据采集的连续性，形成了海量的数据信息，所以建立一个历史数据库，可以为交通状态预测提供参考数据，同时有利于减少冗余信息. 本文基于交通信息的相关性和相似性特性，提出了根据浮动车采集的历史数据建立历史标准数据库的方法，包括数据存储的时间粒度划分方法和两种用于计算基本数据序列的算法，即直接求和判断法和综合评价判断法. 最后利用北京快速路实测浮动车数据进行了实例计算和分析. 计算结果表明，本文提出的算法是有效的. 由于直接求和判断法具有简单、快速的优势，在实际应用中，建议采用直接求和判断法.

Database Building Method of Floating Car Speed with Relativity Analysis

As a new collection technology, the floating car traffic information collection has a broad application. Because of the continuity, the collected traffic information increases rapidly as time going. As a result, it cumulates great of data. It is necessary to build a historical database, which can provide reference for traffic status prediction, and can simultaneously reduce the redundancy data. This paper presents the method for building the standard database using the historical floating car data based on the relativity and comparability of traffic information. It includes how to fix on the data storage time interval and two algorithms for computing the basic data sequence, which named direct sum estimation method and integrative estimation method. Finally, an example is presented by the field floating car data from Beijing expressway. The results indicate the validity of the method. The direct sum estimation method is recommended in the application because of its advantage of simpleness and great efficiency.