The doctoral dissertations of the former Helsinki University of Technology (TKK) and Aalto University Schools of Technology (CHEM, ELEC, ENG, SCI) published in electronic format are available in the electronic publications archive of Aalto University - Aaltodoc.
Aalto

Fuzzy Traffic Signal Control - Principles and Applications

Jarkko Niittymäki

Dissertation for the degree of Doctor of Science in Technology to be presented with the permission of the Department of Civil and Environmental Engineering for public criticism in Auditorium R1 at Helsinki University of Technology, Espoo, Finland on the 31st of January 2002, at 12 noon.

Overview in PDF format (ISBN 951-22-5701-7)   [546 KB]
Errata (in PDF)
Dissertation is also available in print (ISBN 951-22-5784-X)

Abstract

The FUSICO (Fuzzy Signal Control)-research project was started in 1996 at the Helsinki University of Technology. The main goals of the project are theoretical analysis of fuzzy traffic signal control, generalized fuzzy rules using linguistic variables, validation of fuzzy control principles, calibration of membership functions, and development of a fuzzy adaptive signal controller.

This thesis discusses four hypotheses for fuzzy traffic signal control. They are I) generality of fuzzy control, II) competitiveness of fuzzy control III) multilevelity, -dimensionality and -objectiveness and IV) realisticity in real traffic signal control.

The control principles and rules for the fuzzy control are modeled based on the actions of an experienced policeman represented by knowledge of an experienced signal control planner. According to the results the control parameters of fuzzy traffic signal control can be divided into three different groups: traffic volume, capacity, and level of service parameters. The fuzzy control algorithm of isolated traffic signal control can be derived based on these parameters. The fuzzy inference is perhaps the most important part of fuzzy control, but also the methods of fuzzification and defuzzification have to be introduced. Some artificial methods, like genetic algorithms and neural networks, have been tested without any benefits in comparison with the empirical membership functions. The fuzzy similarity method, which is based on Lukasiewicz's logic, has been introduced as a potential defuzzification method.

In the development phase, the testing of fuzzy control has been done by simulation. Several different control strategies have been tested in different isolated control environments. The results of signalized pedestrian crossing indicated that the fuzzy control provides pedestrian friendly control keeping vehicle delay smaller than the conventional control. Based on the experiences of the Pappis-Mamdani control algorithm, a new control algorithm for two-phase vehicle control was developed. According to the statistical tests, the application area of fuzzy control is wide. The results of multi-phase control indicated that the traditional extension principle still is a better traffic signal control mode in the area of very low traffic volumes. However, an application area of fuzzy control is available. The experiences of fuzzy public tranport priorities and fuzzy control on major arterials have been promising.

The multilevel (traffic situation, phase selection and extension inference) fuzzy control makes adaptivity possible. This also means that the number of control programs can be smaller than in the traditional VA-control. The most significant difference between traditional and fuzzy control methods is that the extension principle in VA-control looks at only the green signal groups, but the fuzzy control analyzes also the queues behind the red signal groups. This multi-dimensionality, the opposite input-parameters and the free rule-base development enable the multi-objective control.

Finally, the fuzzy control methods have been tested in several real intersections. The proposed controller consists of traffic and control models, and it is justified that this kind of on-line simulation or simulation based traffic control is a working method. According to the statistical tests of before-and-after studies, the fuzzy control has proven to be a potential control method in real isolated traffic signal control.

This thesis consists of an overview and of the following 8 publications:

  1. Niittymäki J., Kikuchi S. (1998). Application of Fuzzy Logic to the Control of a Pedestrian Crossing Signal. Transportation Research Record No. 1651. Washington D.C. pp. 30-38. © 1998 Transportation Research Board. By permission.
  2. Niittymäki J., Pursula M. (2000). Signal control using fuzzy logic. Fuzzy Sets and Systems, International Journal of Soft Computing, Vol. 116, No 1, November, 2000. pp. 11-22. © 2000 Elsevier Science. By permission.
  3. Niittymäki J. (2001). Installation and Experiences of Field Testing a Fuzzy Signal Controller. European Journal of Operational Research, Vol. 131/2, June 2001. pp. 45-53. © 2001 Elsevier Science. By permission.
  4. Niittymäki J. (1999). Using Fuzzy Logic to Control Traffic Signals at Multi-Phase Intersections. In: Reusch B. (Ed.). Computational Intelligence - Theory and Applications. International Conference, 6th Fuzzy Days, Proceedings. Springer, Berlin-Heidelberg. pp. 354-362.
  5. Niittymäki J. (2001). General Fuzzy Rule Base for Isolated Traffic Signal Control - Rule Formulation. Journal of Transportation Planning and Technology, Vol 24, no 3, August 2001. pp. 227-247. © 2001 Gordon & Breach Science Publishers. By permission.
  6. Niittymäki J., Turunen E. (2001). Traffic Signal Control on total Fuzzy Similarity Based Reasoning. Paper accepted for publication in the Journal of Fuzzy Sets and Systems, International Journal of Soft Computing. (19.7.2001).
  7. Niittymäki J., Könönen V. (2000). Traffic Signal Controller Based on Fuzzy Logic. SMC2000 Conference Proceedings, 2000 IEEE International Conference on Systems, Man & Cybernetics, ISBN 0-7803-6583-6, October 8-11, 2000, Nashville, Tennessee, USA. pp. 3578-3581. © 2001 IEEE. By permission.
  8. Niittymäki J., Mäenpää M. (2001). The role of fuzzy logic public transport priority in traffic signal control. Traffic Engineering and Control, International Journal of Traffic Management and Transportation Planning, January 2001, Vol. 42. No. 1. pp. 22-26. © 2001 TEC, Hemming-Group Ltd. By permission.

Errata of articles P1, P2, and P4

Keywords: transportation engineering, traffic signal control, fuzzy logic, isolated intersections

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© 2002 Helsinki University of Technology

Last update 2011-05-26