Online freeway traffic estimation with real floating car data

Felix Rempe, Philipp Franeck, Ulrich Fastenrath, Klaus Bogenberger

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

17 Scopus citations

Abstract

In this paper, the performance of the well-known Generalized Adaptive Smoothing Method (GASM) as online traffic speed estimator with Floating Car Data (FCD) as single source of data is assessed. Therefore, the main challenges originating from the sparseness and delay in collecting FCD are addressed and a procedure using the GASM is proposed that allows estimating traffic velocities continuously. In a subsequent study, the method is applied to real FCD recorded by a huge fleet of privacy-Aware mobile sensors during a common congestion pattern on German freeway A99. Focus of the study is to assess the accuracy of traffic speed estimation using the online GASM with respect to varying data densities and delays. The result is that the proposed estimator outperforms naïve approaches in almost all considered setups. Significant accuracy gains compared to naïve methods are achieved, especially if the parameter sets are chosen according to the characteristics of given data. Yet, insufficient actuality of data challenges the GASM, revealing new potential for further enhancements of the method.

Original languageEnglish
Title of host publication2016 IEEE 19th International Conference on Intelligent Transportation Systems, ITSC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1838-1843
Number of pages6
ISBN (Electronic)9781509018895
DOIs
StatePublished - 22 Dec 2016
Externally publishedYes
Event19th IEEE International Conference on Intelligent Transportation Systems, ITSC 2016 - Rio de Janeiro, Brazil
Duration: 1 Nov 20164 Nov 2016

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

Conference

Conference19th IEEE International Conference on Intelligent Transportation Systems, ITSC 2016
Country/TerritoryBrazil
CityRio de Janeiro
Period1/11/164/11/16

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