Processing raw data from global positioning systems without additional information

Abstract

Since the first GPS studies in the mid-1990s, this way of surveying individual travel behaviour has gained increasing attention in transport research. Compared to classic travel survey methods researchers benefit from more accurate and reliable information. At the same time, the participants’ burden is reduced substantially if the GPS data collection is not accompanied by elaborate questioning. However, without additional information, such as modes and trip purposes, extensive post-processing is required to derive data that can be used for analysis and model estimation. The corresponding procedures are still an ongoing research issue. This paper describes a post-processing procedure that needs no other input than the most basic GPS raw data: three-dimensional positions and the corresponding timestamps. First, the data is thoroughly cleaned and smoothed. Second, trips and activities are determined. Third, the trips are segmented into single-mode stages and the transport mode for each of the stages is identified. The procedure is applied to GPS records collected in the Swiss cities of Zurich, Winterthur and Geneva. 4882 participants carried an on-person GPS-receiver for 6.65 days on average. The results are compared to the Swiss Microcensus 2005 to demonstrate that derived data is ready for further applications, such as discrete choice model estimations.