What is an Appropriate Temporal Sampling Rate to Record Floating Car Data with a GPS?

ISPRS International Journal of Geo-Information

Peter Ranacher³  Richard Brunauer¹  Stefan van der Spek²  Siegfried Reich¹ 

[1] Salzburg Research Forschungsgesellschaft mbH, Jakob Haringer Straße 5/3, 5020 Salzburg, Austria;Department of Urbanism, Faculty of Architecture, Delft University of Technology, Julianalaan 134, 2628BL Delft, The Netherlands;;Department of Geoinformatics - Z_GIS, University of Salzburg, Schillerstraße 30, 5020 Salzburg, Austria

关键词: GPS tracking;    GPS measurement error;    interpolation error;    temporal sampling interval;    movement analysis;    rediscretization;   

DOI  :  10.3390/ijgi5010001

来源: mdpi

【 摘 要 】

Floating car data (FCD) recorded with the Global Positioning System (GPS) are an important data source for traffic research. However, FCD are subject to error, which can relate either to the accuracy of the recordings (measurement error) or to the temporal rate at which the data are sampled (interpolation error). Both errors affect movement parameters derived from the FCD, such as speed or direction, and consequently influence conclusions drawn about the movement. In this paper we combined recent findings about the autocorrelation of GPS measurement error and well-established findings from random walk theory to analyse a set of real-world FCD. First, we showed that the measurement error in the FCD was affected by positive autocorrelation. We explained why this is a quality measure of the data. Second, we evaluated four metrics to assess the influence of interpolation error. We found that interpolation error strongly affects the correct interpretation of the car’s dynamics (speed, direction), whereas its impact on the path (travelled distance, spatial location) was moderate. Based on these results we gave recommendations for recording of FCD using the GPS. Our recommendations only concern time-based sampling, change-based, location-based or event-based sampling are not discussed. The sampling approach minimizes the effects of error on movement parameters while avoiding the collection of redundant information. This is crucial for obtaining reliable results from FCD.

【 授权许可】

CC BY   
© 2016 by the authors; licensee MDPI, Basel, Switzerland.