Scheduling of Traffic Entities Under Reduced Traffic Flow by Means of Fuzzy Logic Control

  • Zdenko Kljaić Ericsson Nikola Tesla d.d.
  • Danijel Pavković University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture
  • Tomislav Josip Mlinarić University of Zagreb, Faculty of Transport and Traffic Sciences
  • Mladen Nikšić University of Zagreb, Faculty of Transport and Traffic Sciences
Keywords: fuzzy logic, simulation, railway traffic, scheduling, logistics, stochastic traffic flow.

Abstract

This paper presents the design of a fuzzy logic-based traffic scheduling algorithm aimed at reducing traffic congestion for the case of partial obstruction of a bidirectional traffic lane. Such a problem is typically encountered in rail traffic and personal rapid transportation systems with predefined and fixed traffic corridors. The proposed proportional-derivative (PD) fuzzy control algorithm, serving as a traffic control automaton, alternately assigns adaptive green light periods to traffic coming from each direction. The proposed fuzzy logic-based traffic controller has been compared with the conventional traffic control automaton featuring fixed-durations of green light intervals. The comparison has been carried out within a simulation environment for four different probability distributions of stochastic traffic flows at each end of the considered traffic corridor. Results have shown that the proposed fuzzy logic-based traffic controller performance is far superior to that of the conventional traffic control law in terms of achieving shorter vehicle queue lengths and less disparity in queue lengths for all considered simulation scenarios.

References

Hegyi A, Bellemans T, De Schutter B. Freeway traffic management and control. In: Meyers RA. (ed.) Encyclopedia of Complexity and Systems Science. New York, USA: Springer; 2009. p. 3943-3964.

Otto A, et al. Risk reduction partnerships in railway transport infrastructure in an alpine environment. International Journal of Disaster Risk Reduction. 2019;33: 385-397.

Siu LK. A Review of Alternative and Innovative Transit Systems. HKIE Transactions. 2006;13(1): 41-46.

Lowson M. Personal public transport. Proceedings of the Institution of Civil Engineering and Transportation. 1999;135: 139-151.

Lowson M. Sustainable personal transport. Proceedings of the Institution of Civil Engineering – Municipal Engineer. 2002;151(1): 73-82.

Telefónica presents the first 5G use case with autonomous driving and content consumption. Press release. Available from: https://www.telefonica.com/en/web/press-office/-/telefonica-presents-the-first-5g-use-case-with-autonomous-driving-and-content-consumption [Accessed 21st July 2020].

Muszynski P, Oates R. Conceptual Design and Accomplishment of a Steel Guideway for the Personal Rapid Transit at Heathrow Airport in London, UK. Structural Engineering International. 2011;20(1): 21-25.

Lees-Miller JD, Wilson RE. Proactive empty vehicle redistribution for personal rapid transit and taxis. Transportation Planning and Technology. 2012;35(1): 17-30.

Wang SJ, Moriarty P, Ji YM, Chen Z. A new approach for reducing urban transport energy. Energy Procedia. 2015;75: 2910-2915.

Mlinarić TJ, Đorđević, B, Krmac E. Evaluating framework for key performance indicators or railway ITS. Promet – Traffic&Transportation. 2018;30(4): 491-500.

Shen Y, Ren G, Liu Y. Timetable Design for Minimizing Passenger Travel Time and Congestion for a Single Metro Line. Promet – Traffic&Transportation. 2018;30(1): 21-33.

Hoyer R, Jumar U. An advanced fuzzy controller for traffic lights. In: Crespo A. (ed.) Proceedings of IFAC Artificial Intelligence in Real Time Control, 3-5 October 1994, Valencia, Spain. Oxford, UK: Elsevier Science Ltd; 1994. p. 67-72.

Trabio MB, Kaseko MS, Ande M. A two-stage fuzzy logic controller for traffic lights. Transportation Research Part C. 1999;7(6): 353-367.

Chou C-H, Teng J-C. A fuzzy logic controller for traffic junction signals. Information Sciences. 2002;143(1-4): 73-97.

Dörterler M, Bay ÖF. Neural Network Based Vehicular Location Prediction Model for Cooperative Active Safety Systems. Promet – Traffic&Transportation. 2018;30(2): 205-215.

Bortas I, Brnjac N, Dundović Č. Transport Routes Optimization Model through Application of Fuzzy Logic. Promet – Traffic&Transportation. 2018;30(1): 121-129.

Kljaić Z, et al. Fuzzy logic-based scheduling of rail vehicles under reduced traffic flow conditions. In: Panić Z, Despotović D. (eds.) Proceedings of 27th Telecommunications Forum TELFOR 2019, 26-27 November 2019, Belgrade, Serbia. Piscataway, NJ, US: IEEE Press; 2019. Paper No. 4484, 4 pages.

Corman F, D’Ariano A, Pacciarelli D, Pranzo M. Evaluation of green wave policy in real-time railway traffic management. Transportation Research Part C. 2009;17(6): 607-616.

Ross TJ. Fuzzy Logic with Engineering Applications. Chichester, UK: John Wiley & Sons; 2004.

Otto SR, Denier JP. An Introduction to Programming and Numerical Methods in MATLAB. London, UK: Springer-Verlag; 2005.

Marques de Sá JP. Applied Statistics Using SPSS, STATISTICA, MATLAB and R. Berlin-Heidelberg, Germany: Springer-Verlag; 2007.

Branston D. Models of single lane time headway distribution. Transportation Science. 1976;10(2): 125-148.

Published
2021-08-05
How to Cite
1.
Kljaić Z, Pavković D, Mlinarić TJ, Nikšić M. Scheduling of Traffic Entities Under Reduced Traffic Flow by Means of Fuzzy Logic Control. Promet - Traffic&Transportation. 2021;33(4):621-32. DOI: 10.7307/ptt.v33i4.3686
Section
Articles