Abstract
Metering of merging traffic flows from on-ramp section of freeways is an important research issue for traffic engineers. Although metering signal is one of the recent applications for the subject, assignment of signal timing is problematic. The problem is based on dynamic structure of traffic flows and uncertainties coming up from driver behaviors. Because of variations in car following behavior and perception-reaction times of drivers, uncertainties are occurred. To handle these uncertainties, fuzzy logic approach is preferred in this research. A Fuzzy LogicControl based Dual Lane Ramp Metering (FuLCRMe) Model is proposed. The model considers following parameters as inputs; arrival headways of mainline, queue length at ramp and red time of ramp. Decision about red signal timing is made using these parameters. Based on this decision the final red time is assigned. The FuLCRMe model is tested by a simulation developed in Microsoft Excel program considering different cases. Results of the comparisons show that the FuLCRMe model provides significant decrease in delays, queue length, cycle time, CO\(_{2}\) emission, fuel consumption, travel time and total cost.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Papageorgiou, M., Blosseville, J.M., Hadj-Salem, H.: Modelling and real-time control of traffic flow on the southern part of Boulevard Peripherique in Paris-Part I modeling. Transp. Res. A 24, 345–359 (1990)
Papageorgiou, M., Hadj-Salem, H., Middelham, F.: ALINEA local ramp metering summary of field results. Transportation Research Record Journal of the Transportation Research Board No 1603, TRB of National Academies, Washington D.C., 90–98, (1997)
Taylor, C., Meldrum, D.: Freeway traffic data prediction via artificial neural networks for use in a fuzzy logic ramp metering algorithm. In: Proceedings of the Intelligent Vehicles ’94 Symposium, pp. 308–313, 24–26 Oct 1994.
Murat, Y.S., Gedizlioğlu, E.: A fuzzy logic multi-phased signal control model for isolated junctions. Transp. Res. Part C Emerg. Technol. 13/1:19–36, Pergamon Press (2005).
Murat, Y.S., Gedizlioğlu, E.: Investigation of vehicle time headways in Turkey. In: Proceedings Of the Institution Of Civil Engineers-Transport, Vol. 160(2), pp. 73–78 (2007)
Murat, Y.S., Uludag, N.: Route choice modelling in urban transportation networks using fuzzy logic and logistic regression methods. J. Sci. Ind. Res. 67(1), 19–27 (2008)
Murat, Y.S., Kulak, O.: Use of information axiom for route choice in transportation networks. Pamukkale Univ. J. Eng. Sci. (PAJES) 11(3), 425–435 (2005)
Taylor, C., Meldrum, D., Jacobson, L.: Fuzzy ramp metering design overview and simulation results transportation research record. Journal of Transportation Research Board No 1634, TRB of National Academies, Washington D.C., 10–19 (1998)
Chen, X., Tian, A.: Research on fuzzy on-ramp metering and simulation in urban expressway. In: International Conference on Optoelectronics and Image Processing (ICOIP), 2010, Vol. 2, pp. 221–224, (2010) (Publication Year)
Acknowledgments
Authors of this paper would like to thank Dr. Rahmi Akcelik for providing SIDRA Intersection program. On the other hand, support of Pamukkale University Scientific Research Project Coordination Department by the project number 2010FBE061 is appreciated.
Open Access
This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Murat, Y.S., Cakici, Z., Yaslan, G. (2014). Use of Fuzzy Logic Traffic Signal Control Approach as Dual Lane Ramp Metering Model for Freeways. In: Snášel, V., Krömer, P., Köppen, M., Schaefer, G. (eds) Soft Computing in Industrial Applications. Advances in Intelligent Systems and Computing, vol 223. Springer, Cham. https://doi.org/10.1007/978-3-319-00930-8_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-00930-8_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-00929-2
Online ISBN: 978-3-319-00930-8
eBook Packages: EngineeringEngineering (R0)