TY - GEN
T1 - Online freeway traffic estimation with real floating car data
AU - Rempe, Felix
AU - Franeck, Philipp
AU - Fastenrath, Ulrich
AU - Bogenberger, Klaus
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/12/22
Y1 - 2016/12/22
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85010073216&partnerID=8YFLogxK
U2 - 10.1109/ITSC.2016.7795854
DO - 10.1109/ITSC.2016.7795854
M3 - Conference contribution
AN - SCOPUS:85010073216
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 1838
EP - 1843
BT - 2016 IEEE 19th International Conference on Intelligent Transportation Systems, ITSC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Conference on Intelligent Transportation Systems, ITSC 2016
Y2 - 1 November 2016 through 4 November 2016
ER -