TY - GEN
T1 - Fusing probe speed and flow data for robust short-term congestion front forecasts
AU - Rempe, Felix
AU - Kessler, Lisa
AU - Bogenberger, Klaus
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/8/8
Y1 - 2017/8/8
N2 - In this paper a robust and flexible method is proposed that combines the strengths of detector as well as Floating Car (FC) data in order to provide short-term congestion front forecasts. Based on the high spatio-temporal resolution of FC data, congested regimes and according congestion fronts are identified accurately. Subsequently, the flow data provided by loop detectors are utilized in order to predict these congestion fronts for a time horizon of up to ten minutes. Three variations of the method are presented which focus the difficulty of estimating traffic density in congested traffic conditions with given data. The evaluation is based on real FC as well as loop detector data collected during a congestion on the German Autobahn A9. Comparisons of the variants of the proposed method and a naive predictor emphasize the advantage of combining both data sources and point out the strategy that results in the most accurate front forecasts.
AB - In this paper a robust and flexible method is proposed that combines the strengths of detector as well as Floating Car (FC) data in order to provide short-term congestion front forecasts. Based on the high spatio-temporal resolution of FC data, congested regimes and according congestion fronts are identified accurately. Subsequently, the flow data provided by loop detectors are utilized in order to predict these congestion fronts for a time horizon of up to ten minutes. Three variations of the method are presented which focus the difficulty of estimating traffic density in congested traffic conditions with given data. The evaluation is based on real FC as well as loop detector data collected during a congestion on the German Autobahn A9. Comparisons of the variants of the proposed method and a naive predictor emphasize the advantage of combining both data sources and point out the strategy that results in the most accurate front forecasts.
UR - http://www.scopus.com/inward/record.url?scp=85030262865&partnerID=8YFLogxK
U2 - 10.1109/MTITS.2017.8005695
DO - 10.1109/MTITS.2017.8005695
M3 - Conference contribution
AN - SCOPUS:85030262865
T3 - 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2017 - Proceedings
SP - 31
EP - 36
BT - 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2017
Y2 - 26 June 2017 through 28 June 2017
ER -