Real Time Identification of Road Traffic Control Measures

Khaled Almejalli, Keshav Dahal, M. Alamgir Hossain

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The operator of a traffic control centre has to select, the most appropriate traffic control action or combination of actions in a short time to manage the traffic network when non-recurrent road traffic congestion happens. This is a complex task, which requires expert knowledge, much experience and fast reaction. There are a large number of factors related to a traffic state as well as a large number of possible control actions that need to be considered during the decision making process. The identification of suitable control actions for a given non-recurrent traffic congestion call be tough even for experienced operators. Therefore, simulation models are used in many cases. However, simulating different traffic actions for it number of control measures In a complicated situation is very time-consuming. This chapter presents an intelligent method for the real-time identification of road traffic actions which assists the human operator of the traffic control centre in managing the current traffic state. The proposed system combines three soft-computing approaches, namely fuzzy logic, neural net-works, and genetic algorithms. The system employs a fuzzy-neural network tool with self-organization algorithm for initializing the membership functions, a genetic algorithm (GA) for identifying fuzzy rules, and (lie back-propagation neural network algorithm for fine tuning the system parameters. The proposed system has been tested for a case-study of a small section of the ring-road around Riyadh city in Saudi Arabia. The results obtained for the case study are promising and demonstrate that the proposed approach can provide an effective support for real-time traffic control.
Original languageEnglish
Title of host publicationAdvances in Computational Intelligence in Transport, Logistics, and Supply Chain Management
EditorsAndreas Fink, Franz Rothlauf
PublisherSpringer-Verlag Berlin
Pages63-80
Volume144
ISBN (Print)978-3-540-69024-5
DOIs
Publication statusPublished - 2008
Externally publishedYes

Publication series

NameStudies in Computational Intelligence

Keywords

  • Road traffic control
  • Fuzzy logic
  • Neural networks
  • Genetic algorithms

Cite this

Almejalli, K., Dahal, K., & Hossain, M. A. (2008). Real Time Identification of Road Traffic Control Measures. In A. Fink, & F. Rothlauf (Eds.), Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management (Vol. 144, pp. 63-80). (Studies in Computational Intelligence). Springer-Verlag Berlin. https://doi.org/10.1007/978-3-540-69390-1
Almejalli, Khaled ; Dahal, Keshav ; Hossain, M. Alamgir. / Real Time Identification of Road Traffic Control Measures. Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management. editor / Andreas Fink ; Franz Rothlauf. Vol. 144 Springer-Verlag Berlin, 2008. pp. 63-80 (Studies in Computational Intelligence).
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Almejalli, K, Dahal, K & Hossain, MA 2008, Real Time Identification of Road Traffic Control Measures. in A Fink & F Rothlauf (eds), Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management. vol. 144, Studies in Computational Intelligence, Springer-Verlag Berlin, pp. 63-80. https://doi.org/10.1007/978-3-540-69390-1

Real Time Identification of Road Traffic Control Measures. / Almejalli, Khaled; Dahal, Keshav; Hossain, M. Alamgir.

Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management. ed. / Andreas Fink; Franz Rothlauf. Vol. 144 Springer-Verlag Berlin, 2008. p. 63-80 (Studies in Computational Intelligence).

Research output: Chapter in Book/Report/Conference proceedingChapter

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T1 - Real Time Identification of Road Traffic Control Measures

AU - Almejalli, Khaled

AU - Dahal, Keshav

AU - Hossain, M. Alamgir

PY - 2008

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N2 - The operator of a traffic control centre has to select, the most appropriate traffic control action or combination of actions in a short time to manage the traffic network when non-recurrent road traffic congestion happens. This is a complex task, which requires expert knowledge, much experience and fast reaction. There are a large number of factors related to a traffic state as well as a large number of possible control actions that need to be considered during the decision making process. The identification of suitable control actions for a given non-recurrent traffic congestion call be tough even for experienced operators. Therefore, simulation models are used in many cases. However, simulating different traffic actions for it number of control measures In a complicated situation is very time-consuming. This chapter presents an intelligent method for the real-time identification of road traffic actions which assists the human operator of the traffic control centre in managing the current traffic state. The proposed system combines three soft-computing approaches, namely fuzzy logic, neural net-works, and genetic algorithms. The system employs a fuzzy-neural network tool with self-organization algorithm for initializing the membership functions, a genetic algorithm (GA) for identifying fuzzy rules, and (lie back-propagation neural network algorithm for fine tuning the system parameters. The proposed system has been tested for a case-study of a small section of the ring-road around Riyadh city in Saudi Arabia. The results obtained for the case study are promising and demonstrate that the proposed approach can provide an effective support for real-time traffic control.

AB - The operator of a traffic control centre has to select, the most appropriate traffic control action or combination of actions in a short time to manage the traffic network when non-recurrent road traffic congestion happens. This is a complex task, which requires expert knowledge, much experience and fast reaction. There are a large number of factors related to a traffic state as well as a large number of possible control actions that need to be considered during the decision making process. The identification of suitable control actions for a given non-recurrent traffic congestion call be tough even for experienced operators. Therefore, simulation models are used in many cases. However, simulating different traffic actions for it number of control measures In a complicated situation is very time-consuming. This chapter presents an intelligent method for the real-time identification of road traffic actions which assists the human operator of the traffic control centre in managing the current traffic state. The proposed system combines three soft-computing approaches, namely fuzzy logic, neural net-works, and genetic algorithms. The system employs a fuzzy-neural network tool with self-organization algorithm for initializing the membership functions, a genetic algorithm (GA) for identifying fuzzy rules, and (lie back-propagation neural network algorithm for fine tuning the system parameters. The proposed system has been tested for a case-study of a small section of the ring-road around Riyadh city in Saudi Arabia. The results obtained for the case study are promising and demonstrate that the proposed approach can provide an effective support for real-time traffic control.

KW - Road traffic control

KW - Fuzzy logic

KW - Neural networks

KW - Genetic algorithms

U2 - 10.1007/978-3-540-69390-1

DO - 10.1007/978-3-540-69390-1

M3 - Chapter

SN - 978-3-540-69024-5

VL - 144

T3 - Studies in Computational Intelligence

SP - 63

EP - 80

BT - Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management

A2 - Fink, Andreas

A2 - Rothlauf, Franz

PB - Springer-Verlag Berlin

ER -

Almejalli K, Dahal K, Hossain MA. Real Time Identification of Road Traffic Control Measures. In Fink A, Rothlauf F, editors, Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management. Vol. 144. Springer-Verlag Berlin. 2008. p. 63-80. (Studies in Computational Intelligence). https://doi.org/10.1007/978-3-540-69390-1