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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
28.10.2015
DOI
https://doi.org/10.7307/ptt.v27i5.1667
ISSUE
2015 (Vol 27), Issue 5
LICENSE
Copyright (c) 2024 Ondrej Pribyl, Milan Koukol, Jana Kuklova
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This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Computational Intelligence in Highway Management: A Review

Authors:

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,

Abstract

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.

References

  1. 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. Decembe

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How to Cite
Pribyl, O. (et al.) 2015. Computational Intelligence in Highway Management: A Review. Traffic&Transportation Journal. 27, 5 (Oct. 2015), 439-450. DOI: https://doi.org/10.7307/ptt.v27i5.1667.

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