Computational Intelligence in Highway Management: A Review

  • Ondrej Pribyl Associate professor, MSc, PhD Vice-dean for international relations Czech Technical University in Prague Department of Applied Mathematics Na Florenci 25, Praha 1, 110 00 Czech Republic
  • Milan Koukol Czech Technical University in Prague Faculty of Transportation Sciences
  • Jana Kuklova Czech Technical University in Prague Faculty of Transportation Sciences
Keywords: traffic management, traffic control systems, congestion, soft computing, multi-agent systems,


Highway management systems are used to improve safety and driving comfort on highways by using control strategies and providing information and warnings to drivers. They use several strategies starting from speed and lane management, through incident detection and warning systems, ramp metering, weather information up to, for example, informing drivers about alternative roads. This paper provides a review of the existing approaches to highway management systems, particularly speed harmonization and ramp metering. It is focused only on modern and advanced approaches, such as soft computing, multi-agent methods and their interconnection. Its objective is to provide guidance in the wide field of highway management and to point out the most relevant recent activities which demonstrate that development in the field of highway management is still important and that the existing research exhibits potential for further enhancement.

Author Biography

Ondrej Pribyl, Associate professor, MSc, PhD Vice-dean for international relations Czech Technical University in Prague Department of Applied Mathematics Na Florenci 25, Praha 1, 110 00 Czech Republic
Associate professor, MSc, PhD Vice-dean for international relations


Přibyl O. An integrated model predictive highway management system. 1st ed. VDM Verlag; 2010.

Srinivasan D, Sanyal S, Tan W.W. Hybrid Neuro-Fuzzy Technique for Automated Traffic Incident Detection. Proceedings of the International Joint Conference on Neural Networks (IJCNN); 2006 July 16-21; Vancouver, Canada; 2006. p. 713-719.

Ma Y, Chowdhury M, Jeihani M, Fries R. Accelerated incident detection across transportation networks using vehicle kinetics and support vector machine in cooperation with infrastructure agents. IET Intelligent Transport Systems. 2010;4(4):328-337.

Hegyi A, De Schutter B, Hellendoorn H. Model predictive control for optimal coordination of ramp metering and variable speed limits. Transportation Research C: Emerging Technologies. 2005;13(3):185-209.

Wang W, Xu W, Yang Z, Zhao D. Research on a Support-Vector-Machine-Based Variable Speed Limits Control Model. Proc. Int. Conf. Transportation, Mechanical, and Electrical Engineering (TMEE). Changchun. China. December 2011:2234-2238.

Pang MB, He GG. Chaos Rapid Recognition of Traffic Flow by Using Rough Set Neural Network. Proceedings of International Symposium on Information Processing (ISIP); Moscow, Russia; May 2008. p. 168 172.

Přibyl O. Computational intelligence in transportation: Short user-oriented guide. In: Goulias, KG, editor. Transport Science and Technology. Elsevier, 200; p. 37-54.

Jang JSR, Sun C-T, Mizutani, E. Neuro-fuzzy and soft Computing. Prentice Hall; 1997.

Loia V, editor. Soft Computing Agents. A New Perspective for Dynamic Information Systems. Vol. 83 of Frontiers in Artificial Intelligence and Applications. IOS Press; 2003.

Bundesanstalt für Straßenwessen. MARZ: Merkblatt für die Ausstattung von Verkehrsrechnerzentralen und Unterzentralen. Dieses Merkblatt wurde aufgestellt von einem Bund/Länder-Arbeitskreis unter Leitung der BASt; 1999.

Kühne RD, Langbein K. Optimierung der Parameter einer Linienbeein-fl ussungsanlage. In: Tagungsbericht Heureka 93. Karlsruhe 1993; p. 116-139.

Gu S, Ma J, Wang J, Sui X, Liu Y. Methodology for Variable Speed Limit Activation in Active Traffic Management. Procedia – Social and Behavioral Sciences. 2013;96:2129 2137.

Allaby P, Hellinga B, Bullock M. Variable Speed Limits: Safety and Operational Impacts of a Candidate Control Strategy for Freeway Applications. IEEE Transactions on Intelligent Transportation Systems. 2007;8(4):671-680.

Jo Y, Kim Y, Jung I. Variable Speed Limit to Improve Safety near Traffic Congestion on Urban Freeways. International Journal of Fuzzy Systems. 2012;14(2):278-288.

Vukanovic S, Kates R, Denaes S, Keller H. A novel algorithm for optimized, safety oriented dynamic speed regulation on highways: INCA. Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems; 2005 Sep 13-15; Vienna, Austria; 2005. p. 378 383.

Sasaki T, Akiyama T. Development of fuzzy traffic control system on urban expressway. Preprints 5th IFAC/IFIP/IFORS Int. Conf. Transportation Systems. Vienna. Austria; July 1986. p. 333-338.

Sasaki T. Akiyama T. Fuzzy on-ramp control model on urban expressway and its extension. In: Gartner NH, Wilson NHM, editors. Transportation and traffic theory. Elsevier, 1987; p. 377-395.

Wang Y, Bin W, Hui Z. The National Freeway Control System – Further Development with Fuzzy Logic Theory. Proc. IEEE Int. Conf. Industrial Technology. Guangzhou. China; December 1994. p. 729-733.

Bellman R. Adaptive Control Processes. A Guided Tour. Princeton University Press; 1961.

Placer J, Sagahyroon A. Fuzzy Variable Speed Limit Device Project. Report No. FHWA-AZ98-466. Arizona Department of Transportation; 1998.

Placer J. Fuzzy Variable Speed Limit Device Modification and Testing – Phase II. Report No. AZ-466(2). Arizona Department of Transportation; 2001.

Roshandeh AM, Puan OC, Joshani M. Data Analysis Application for Variable Message Signs Using Fuzzy Logic in Kuala Lumpur. Int. Journal of Systems Applications. Engineering & Development. 2009;3(1):18-27.

Chiou YC, Lan LW. Genetic fuzzy logic controller: an iterative evolution algorithm with new encoding method. Fuzzy Sets and Systems. 2005;152(3):617-635.

Chiou YC, Huang YF, Lin PC. Optimal variable speed-limit control under abnormal traffic conditions. Journal of The Chinese Institute of Engineers. 2012;35(3):299 308.

Hadj-Salem H, Blosseville JM, Papageorgiou M. ALINEA. A local feedback control law for on-ramp metering; a real-life study. Proc. 3rd Int. Conf. Road Traffic Control. London, UK; May 1990. p. 194-198.

Papageorgiou M, Hadj-Salem H, Blosseville JM. ALINEA: A local feedback control law for on-ramp metering. Transport Research Record. 1991;1320:58-64.

Papamichail I, Papageorgiou M. Traffic-Responsive Linked Ramp-Metering Control. IEEE Transactions on Intelligent Transportation Systems. 2008;9(1):111-121.

Hadi MA. Coordinated traffic responsive ramp metering strategies – An assessment based on previous studies. Proc. 12th World Congress on ITS. San Francisco, USA. November 2005.

Jacobson L, Stribiak J, Nelson L, Sallman D. Ramp Metering and Control Handbook. Report No. FHWA-HOP-06-001. U.S. Department of Transportation; 2006.

Lu GX, Liu H. Ramp metering. Transportation Research Circular: Artificial Intelligence Applications to Critical Transportation Issues. 2012;E-C168:70-75.

Murat, YS, Cakici Z, Yaslan G. Use of Fuzzy Logic Traffic Signal Control Approach as Dual Lane Ramp Metering Model for Freeways. Proc. 17th Online World Conf. Soft Computing in Industrial Applications. Ostrava, Czech Republic; December 2012. p. 339-349.

Akçelik R. Issues in performance assessment of sign-controlled intersections. Proc. 25th ARRB Conf. Perth, Australia; September 2012.

Hsu, TP, Hsieh TH. Development of Ramp Metering Using Fuzzy Logic Control Algorithm on Freeway. Proc. Eastern Asia Society for Transportation Studies. vol. 9; 2013.

Jiang T, Liang X. Fuzzy Self-Adaptive PID Controller for Freeway Ramp Metering. Proc. Int. Conf. Measuring Technology and Mechatronics Automation (ICMTMA). Zhangjiajie, China; April 2009. p. 570-573.

Zhang HM, Ritchie SG. Freeway ramp metering using artificial neural networks. Transportation Research Part C: Emerging Technologies. 1997;5(5):273-286.

Feng Ch, Yuanhua J, Jian L, Huixin Y, Zhonghai N. Design of Fuzzy Neural Network Control Method for Ramp Metering. Proc. 3rd Int. Conf. Measuring Technology and Mechatronics Automation (ICMTMA). Shanghai, China; January 2011. p. 966-969.

Ghods AH, Kian AR, and Tabibi M. A Genetic-Fuzzy Control Application to Ramp Metering and Variable Speed Limit Control. Proc. IEEE Int. Conf. Systems. Man and Cybernetics. Montreal, Canada; October 2007. p. 1723-1728.

Ghods AH, Kian AR, Tabibi M. Adaptive Freeway Ramp Metering and Variable Speed Limit Control: A Genetic-Fuzzy Approach. IEEE Intelligent Transportation Systems Magazine. 2009;1(1):27-36.

Messmer A, Papageorgiou M. METANET: A macroscopic simulation program for motorway networks. Traffic Engineering & Control. 1990;31(9):466-470.

Yu XF, Xu WL, Alam F, Potgieter J, Fang CF. Genetic fuzzy logic approach to local ramp metering control using microscopic traffic simulation. Proc. 19th Int. Conf. Mechatronics and Machine Vision in Practice (M2VIP). Auckland, New Zealand; November 2012. p. 290-297.

Lu XY, Varaiya P, Horowitz R, Su D, Shladover SE. A New Approach for Combined Freeway Variable Speed Limits and Coordinated Ramp Metering. Proc. 13th Int. IEEE Conf. Intelligent Transportation Systems (ITSC). Funchal, Portugal; September 2010. p. 491-498.

Roseman D. Incident manager control concept multi-agency coordinated traffic management Santa Monica Freeway Smart Corridor. Proc. 64th ITE Annual Meeting Compendium of Technical Papers. Dallas, USA; 1994. p. 528-532.

Logi F, Ritchie SG. A multi agent architecture for cooperative inter jurisdictional traffic congestion management. Transportation Research Part C: Emerging Technologies. 2002;10(5–6):507-527.

Logi F. CARTESIUS: A Cooperative Approach to Real-Time Decision Support for Multi-Jurisdictional Traffic Congestion Management [PhD thesis]. University of California; 1999.

van Katwijk RT, van Koningsbruggen P. Coordination of traffic management instruments using agent technology. Transportation Research Part C: Emerging Technologies. 2002;10(5–6):455-471.

van Katwijk RT, van Koningsbruggen P, De Schutter B, Hellendoorn J. A Test Bed for Multiagent Control Systems in Road Traffic Management. In: Klügl F, Bazzan A, Ossowski S, editors. Applications of Agent Technology in Traffic and Transportation Research Record. Birkhäuser, 2005; p. 113-131.

Hernández JZ, Ossowski S, García-Serrano A. Multiagent Architectures for Intelligent Traffic Management Systems. Transportation Research Part C: Emerging Technologies. 2002;10(5–6):473-506.

Cuena J, Hernández JZ, Molina M. Knowledge-based models for adaptive traffic management systems. Transportation Research Part C: Emerging Technologies. 1995;3(5):311-337.

Cuena, J, Hernández JZ, Molina M. Knowledge oriented design of an application for real time traffic management: The TRYS system. Proc. European Conf. Artificial Intelligence (ECAI). Budapest, Hungary; August 1996. p. 308-312.

Ossovski S, Cuena J, García-Serrano A. A Case of Multiagent Decision Support: Using Autonomous Agents for Urban Traffic Control. Proc. 6th Ibero-American Conf. AI. Lisbon, Portugal; October 1998. p. 100-111.

Ossovski S. Road Traffic Management. In: Co-ordination in Artificial Agent Societies. Berlin, Heidelberg: Springer-Verlag, 1999; p. 153-162.

Tomás VR, Garcia LA. A Cooperative Multiagent System for Traffic Management and Control. Proc. 4th Int. Joint Conf. Autonomous Agents and Multiagent Systems (AAMAS). Utrecht, Netherlands; July 2005. p. 52-59.

van Katwijk RT, De Schutter B, Hellendoorn J. Multi-Agent Coordination of Traffic Control Instruments. Proc. 1st Int. Conf. Infrastructure Systems and Services: Building Networks for a Brighter Future (INFRA). Rotterdam, Netherlands; November 2008. p. 1-6.

Martí I, Tomás VR, Saez A, Martínez JJ. A Rule-based Multi-agent System for Road Traffic Management. Proc. IEEE/WIC/ACM Int. Joint Conf. Web Intelligence and Intelligent Agent Technologies. Milan, Italy; September 2009. p. 595-598.

Almejalli K, Dahal K, Hossain A. An Intelligent Multi-agent Approach for Road Traffic Management Systems. Proc. 18th IEEE Int. Conf. Control Applications (CCA). Saint Petersburg, Russia; July 2009. p. 825-830.

Vilenica A, Renz W, Sudeikat J, Lamersdorf W. Multi-Agent-Architecture for Simulating Traffic Management – A Case Study on Highway Networks. Proc. 2nd Int. Workshop on Nonlinear Dynamics and Synchronization (INDS). Klagenfurt, Austria; July 2009. p. 121 127.

Tomás VR, Martí I, Sáez A, Martínez JJ. Agent-based test-bed for road information systems. IET Intelligent Transport Systems. 2012;6(4):404 412.

Zhang X, Onieva E, Perallos A, Osaba E, Lee VCS. Hierarchical fuzzy rule-based system optimized with genetic algorithms for short term traffic congestion prediction. Transportation Research Part C: Emerging Technologies. June 2014;43(1):127-142.

How to Cite
Pribyl O, Koukol M, Kuklova J. Computational Intelligence in Highway Management: A Review. Promet [Internet]. 2015Oct.28 [cited 2023Jun.6];27(5):439-50. Available from: