Travel surveys offer insights into travel patterns and behavior in a given area and play a critical role in transportation planning efforts. Household travel surveys have traditionally been conducted using diaries and Computer-Assisted Telephone Interview (CATI) techniques. While these surveys provide inputs for travel demand modeling, the increasing use of technology to gather the required information presents new challenges for data processing and analysis. As agencies leverage devices such as GPS loggers and smartphones to collect passive GPS data, the need arises for “back end” algorithms and more advanced GIS techniques to convert raw travel traces into accurate and robust trip information. This task is complicated by issues such as blocked satellite signals, cold-start signal acquisition delays, and other problems that contribute to GPS data gaps and spottiness. The proposed paper will present practical and innovative algorithms and GIS analysis techniques used to automate the processing of GPS travel data in Texas Household Travel Surveys. The procedures discussed considered trip attributes derived from GPS data, as well as their spatial relationship with network links, to automate trip extraction procedures. Geoprocessing parameters, means of identifying trip ends and trip purpose, and methods of statistical analysis will be presented in the paper. This discussion will focus on the use ESRI ArcGIS and the Python developer environment. Evaluation and comparison of results against manually-processed ‘ground-truthing’ data will also be described.