文中研究 GPS 出行调查数据的处理分析和出行 － 活动信息的提取技术． 通过改进静止点法，提出基于连续数据段的状态识别方法，设计了由划分状态段、识别活动和识别出行 3 个步骤组成的出行-活动识别算法，并在此基础上将停留时间阈值设定为 4 min，对中途驻停进行识别． 与纸质调查填报出行信息相比的误差计算结果表明，所设计的算法具有较高的识别精度，可以实现出行、活动及中途驻停的整体识别，即能识别出传统出行调查中被调查者漏报的短时驻停． 文中的研究结论对于提高出行调查效率和数据精度、促进城市交通系统的健康发展具有重要意义．

Dealt with in this paper are the techniques for processing data and identifying trips and activities based on GPS travel survey data． In the investigation，by improving the method of stationary points，a method to identify the status based on continuous data segments is proposed，and a trip-activity identification algorithm consisting of three steps，namely dividing status segments，identifying activities and recognizing trips，is designed． Moreover，by setting the minimum dwell time as 4min，the identification of intermediate stops is performed． Finally，a compari-son is made between the results obtained by the proposed algorithm and the travel diaries reported in paper-form travel survey． It is found that the proposed algorithm is effective in identifying trips，activities and intermediate stops with high accuracy，especially the short-time intermediate stops often omitted in the paper-form travel survey．This research significantly enhances the efficiency and accuracy of travel survey and facilitates the development of urban transportation system．