nach oben

Erschienen in:

2014 | OriginalPaper | Buchkapitel

Artificial Bee Colony-Based Algorithm for Optimising Traffic Signal Timings

verfasst von : Mauro Dell’Orco, Özgür Başkan, Mario Marinelli

Erschienen in: Soft Computing in Industrial Applications

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

This study proposed Artificial Bee Colony (ABC) algorithm for finding optimal setting of traffic signals in coordinated signalized networks for given fixed set of link flows. For optimizing traffic signal timings in coordinated signalized networks, ABC with TRANSYT-7F (ABCTRANS) model is developed. The ABC algorithm is a new population-based metaheuristic approach, and it is inspired by the foraging behavior of honeybee swarm. TRANSYT-7F traffic model is used to estimate total network performance index (PI). The ABCTRANS is tested on medium sized signalized road network. Results showed that the proposed model is slightly better in signal timing optimization in terms of final values of PI when it is compared with TRANSYT-7F in which Genetic Algorithm (GA) and Hill-climbing (HC) methods are exist. Results also showed that the ABCTRANS model improves the medium sized network’s PI by 2.4 and 2.7 % when it is compared with GA and HC methods.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Sustainability Evaluation of Transportation Policies: A Fuzzy-Based Method in a “What to” Analysis

Nächstes Kapitel Use of Fuzzy Logic Traffic Signal Control Approach as Dual Lane Ramp Metering Model for Freeways

Teklu, F., Sumalee, A., Watling, D.: A genetic algorithm approach for optimizing traffic control signals considering routing. Comput. Aided Civil Infrastruct. Eng. 22, 31–43 (2007)CrossRef

Webster, F.V.: Traffic signal settings road research technical paper. No. 39, HMSO, London (1958)

Robertson, D.: TRANSYT’ method for area traffic control. Traffic Eng. Control 10, 276–281 (1969)

Wong, S.C.: Derivatives of the performance index for the traffic model from TRANSYT. Trans. Res. Part B 29(5), 303–327 (1995)CrossRef

Heydecker, B.G.: A decomposed approach for signal optimization in road networks. Trans. Res. Part B 30(2), 99–114 (1996)CrossRef

Wong, S.C., Wong, W.T., Xu, J., Tong, C.O.: A Time-dependent TRANSYT traffic model for area traffic control. In: Proceedings of the Second International Conference on Transportation and Traffic Studies. ICTTS, pp. 578–585 (2000)

Wong, S.C., Wong, W.T., Leung, C.M., Tong, C.O.: Group-based optimization of a time-dependent TRANSYT traffic model for area traffic control. Trans. Res. Part B 36, 291–312 (2002)CrossRef

Girianna, M., Benekohal, R.F.: Application of genetic algorithms to generate optimum signal coordination for congested networks. In: Proceedings of the Seventh International Conference on Applications of Advanced Technologies in Transportation, pp. 762–769 (2002)

Ceylan, H.: Developing combined genetic algorithm–hill-climbing optimization method for area traffic control. J. Trans. Eng. 132(8), 663–671 (2006)CrossRef

10.

Chen, J, Xu, L.: Road-junction traffic signal timing optimization by an adaptive Particle swarm algorithm. In: 9th International conference on control automation robotics and vision, Vol. 1–5, pp. 1103–1109 (2006)

11.

Chiou, S.-W.: A hybrid optimization algorithm for area traffic control problem. J. Oper. Res. Soc. 58, 816–823 (2007)CrossRefMATH

12.

Dan, C., Xiaohong, G.: Study on intelligent control of traffic signal of Urban Area and microscopic simulation. In: Proceedings of the Eighth International Conference of Chinese Logistics and Transportation Professionals, Logistics. The Emerging Frontiers of Transportation and Development in China, pp. 4597–4604 (2008)

13.

Li, Z.: Modeling arterial signal optimization with enhanced cell transmission formulations. J. Trans. Eng. 137(7), 445–454 (2011)CrossRef

14.

Lucic, P., Teodorovic, D.: Transportation modeling: an artificial life approach. In: ICTAI, pp. 216–223 (2002)

15.

Teodorovic, D.: Transport modeling by multi-agent systems: a swarm intelligence approach. Trans. Planning Technol. 26(4), 289–312 (2003)CrossRef

16.

Teodorovic, D., Dell’Orco, M.: Bee colony optimization-a cooperative learning approach to complex transportation problems. In: 10th EWGT Meeting, Poznan, 3–16 Sep 2005

17.

Teodorovic, D., Dell’Orco, M.: Mitigating traffic congestion: solving the ride-matching problem by bee colony optimization. Trans. Planning Technol. 31(2), 135–152 (2008)CrossRef

18.

Karaboga, D.: An idea based on honeybee swarm for numerical optimization. Technical Report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, Turkey (2005)

19.

Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Optim. 39, 459–471 (2007a)MathSciNetCrossRef

20.

Karaboga, D., Basturk, B.: Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. Lecture Notes in Artificial Intelligence, vol. 4529, pp. 789–798, Springer-Verlag, Berlin (2007b)

21.

Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. 8, 687–697 (2008)CrossRef

22.

Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214, 108–132 (2009)MathSciNetCrossRefMATH

23.

Ceylan, H., Bell, M.G.H.: Traffic signal timing optimisation based on genetic algorithm approach, including drivers’ routing. Trans. Res. Part B 38(4), 329–342 (2004)CrossRef

24.

Allsop, R.E., Charlesworth, J.A.: Traffic in a signal-controlled road network: an example of different signal timings including different routings. Traffic Eng. Control 18(5), 262–264 (1977)

25.

Ceylan H (2002) A genetic algorithm approach to the equilibrium network design problem. Ph.D. Thesis, University of Newcastle upon Tyne, UK

Titel: Artificial Bee Colony-Based Algorithm for Optimising Traffic Signal Timings
verfasst von: Mauro Dell’Orco
Özgür Başkan
Mario Marinelli
Verlag: Springer International Publishing
Buch: Soft Computing in Industrial Applications
Print ISBN: 978-3-319-00929-2

Electronic ISBN: 978-3-319-00930-8

Copyright-Jahr: 2014
DOI: https://doi.org/10.1007/978-3-319-00930-8_29

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner