Floating car based travel times for city logistics

Ehmke, Jan Fabian; Meisel, Stephan; Mattfeld, Dirk C.

doi:10.1016/j.trc.2011.11.004

Cited by 73 publications

(36 citation statements)

References 13 publications

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“…Applications in the supply chain coordination domain should be considered. Ehmke et al (2012) show how realtime data can be used to improve decisions in the transportation logistics domain. Dück et al (2012) propose approaches for robust crew and aircraft scheduling, taking into account the delays and disturbances typical for air transportation.…”

Section: Examples Of Initial Resultsmentioning

confidence: 99%

Model-Based Decision Support in Manufacturing and Service Networks

et al. 2014

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Section: Examples Of Initial Resultsmentioning

confidence: 99%

Model-Based Decision Support in Manufacturing and Service Networks

et al. 2014

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“…In this section we first introduce the related work on measurement and interpolation error in movement data (1). Then we show existing filtering and smoothing approaches that aim to remove the effects of error and we discuss movement simulations (2).…”

Section: Related Workmentioning

confidence: 99%

“…FCD are an important data-source in traffic research. FCD allow to calculate time-dependent travel times along urban corridors [1], reveal traffic congestions [2] and unveil the complexity of human mobility [3]. They help to identify flaws in urban traffic planning [4] and to infer traffic states [5].…”

Section: Introductionmentioning

confidence: 99%

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

Ranacher

Brunauer

Spek

et al. 2016

IJGI

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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.

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“…However, their data collection methods have become obsolete due to technological progress. Telematics based data collection nowadays provides a sufficient amount of travel time measurements at low costs [12]. • Eglese et al [11] refer to FCD for time-dependent routing in a supra-regional network in the UK.…”

Section: Challenges For City Logistics Service Providersmentioning

confidence: 99%