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

Abstract

Abstract: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.

Räumliche Lärmanalyse anhand von erweiterten Floating-Car-Daten (xFCD)

“…Die Positionsgeräte zeichnen die Bewegung und räumliche Verortung sowie die dazugehörige Zeitkomponente auf. Dabei besteht jeder aufgezeichnete Datenpunkt aus einer Positionskomponente mit dem entsprechenden Zeitstempel und verweist dadurch auf die räumlich-zeitliche Position des Sensors (Ranacher et al 2016). Eine Erweiterung zu den soeben erläuterten FCD stellen sogenannte Extended Floating Car Data (xFCD) dar.…”

Identification of parking spaces from multi‐modal trajectory data

“…Trajectory data are induced with sampling error for numerous reasons (Hsueh & Chen, 2018; Lou et al, 2009; Ranacher, Brunauer, Van der Spek, et al, 2016), and this strongly affects the correct values of the velocity, acceleration, and change of direction of the moving object (Ranacher, Brunauer, Van der Spek, et al, 2016). Hence, a sampling frequency greater than the Nyquist rate is desirable to avoid sampling errors in trajectory data (Hsueh and Chen, 2018; Lou et al, 2009; Ranacher, Brunauer, Van der Spek, et al, 2016).…”

“…Travel mode detection is done after extracting feature values (e.g., velocity, acceleration, and change of direction) from trajectory data, and training a classification algorithm using the extracted feature values (Etemad, Soares Júnior, & Matwin, 2018; Dabiri et al, 2019). Existing methods of travel mode detection struggle with accuracy due to the errors present in the extracted features (Ranacher, Brunauer, Trutschnig, et al, 2016; Ranacher, Brunauer, Van der Spek, et al,2016). For many trajectory data, mode detection accuracy scores drop below 50% due to these errors.…”

Using Cartograms for Visualizing extended Floating Car Data (xFCD)

“…Therefore, a GPS module is mandatory. Depending on the use case, Ranacher et al (2016) propose an acquisition frequency of 1/2 Hz to 1/10 Hz. Within a defined interval, records are acquired and transmitted to a central server.…”