Top

Arabian Journal for Science and Engineering

Published in:

09-01-2021 | Research Article-Civil Engineering

Artificial Intelligence-Based Protocol for Macroscopic Traffic Simulation Model Development

Authors: Imran Reza, Nedal T. Ratrout, Syed M. Rahman

Published in: Arabian Journal for Science and Engineering | Issue 5/2021

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

This study proposes a machine learning-based protocol for developing a TRANSYT-7F model for an urban arterial network. The developed artificial neural network (ANN) method models the queue lengths of TRANSYT-7F using saturation flow, start-up lost time, and platoon dispersion as inputs. The queue lengths of the selected approaches of the study network can be obtained using the ANN model without running the TRANSYT-7F model. The optimum values of the selected parameters (i.e., saturation flow, start-up lost time, and platoon dispersion) were obtained using the genetic algorithm, which ensures minimum difference between the measured queue length and the ANN output (i.e., queue length). Finally, the comparison of the measured queue length and the simulated queue length with the calibrated TRANSYT-7F model revealed that the mean absolute percentage error was less than 2.5% for all approaches of the study network.

previous article Shrinkage Limit Studies from Moisture Content: Electrical Resistivity Relationships of Soils

next article Predicting Bond Strength of FRP Bars in Concrete Using Soft Computing Techniques

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

Feliciani, C.; Gorrini, A.; Crociani, L.; Vizzari, G.: Calibration and validation of a simulation model for predicting pedestrian fatalities at unsignalized crosswalks by means of statistical traffic data. J. Traffic Transp. Eng. Engl. Ed. 7(1), 1–18 (2020). https://doi.org/10.1016/j.jtte.2019.01.004CrossRef

Ratrout, N.T.; Rahman, S.M.; Reza, I.: Calibration of PARAMICS model: application of artificial intelligence-based approach. Arab. J. Sci. Eng. 40, 3459–3468 (2015). https://doi.org/10.1007/s13369-015-1816-5CrossRef

Al-Ahmadi, H.M.; Jamal, A.; Reza, I.; Assi, K.J.; Ahmed, S.A.: Using microscopic simulation-based analysis to model driving behavior: a case study of Khobar-Dammam in Saudi Arabia. Sustainability (2019). https://doi.org/10.3390/su11113018CrossRef

Zhang, L.; Song, Z.; Tang, X.: Signal coordination models for long arterials and grid networks. Transp. Res. Part C Emerg. Technol. 71, 215–230 (2016)CrossRef

Ratrout, N.: Calibration of TRANSYT platoon dispersion algorithm for conditions along major arterials in the cities of Dammam and Al-Khobar Saudi Arabia. Arab. J. Sci. Eng. 21(4A), 479–488 (1996)

Beura, S.K.; Bhuyan, P.K.: Operational analysis of signalized street segments using multi-gene genetic programming and functional network techniques. Arab. J. Sci. Eng. 43, 5365–5386 (2018). https://doi.org/10.1007/s13369-018-3176-4CrossRef

Otković, I.I.; Tollazzi, T.; Šraml, M.: Calibration of microsimulation traffic model using neural network approach. Expert Syst. Appl. 40, 5965–5974 (2013)CrossRef

Reza, I.; Ratrout, N.T.; Rahman, S.M.: Calibration protocol for PARAMICS microscopic traffic simulation model: application of neuro-fuzzy approach. Can. J. Civ. Eng. 43, 361–368 (2016). https://doi.org/10.1139/cjce-2015-0435CrossRef

Dowling, R.; Holland, J.; Huang, A.: California Department of Transportation: Guidelines for Applying Traffic Microsimulation Modeling Software. Dowling Associates, Oakland, CA (2002). http://docplayer.net/13636925-California-department-of-transportation-guidelines-for-applying-traffic-microsimulation-modeling-software.html. Accessed 11 Aug 2020

10.

Liu, Y.: An arterial signal optimization model for intersections experiencing queue spillback and lane blockage. Transp. Res. Part C Emerg. Technol. 19, 130–144 (2011)CrossRef

11.

Li, M.T.; Gan, A.C.: Signal timing optimization for oversaturated networks using TRANSYT-7F. Transp. Res. Rec. (1999). https://doi.org/10.3141/1683-15CrossRef

12.

Chang, T.H.; Sun, G.Y.: Modeling and optimization of an oversaturated signalized network. Transp. Res. Part B: Methodol. 38(8), 687–707 (2004). https://doi.org/10.1016/j.trb.2003.08.002CrossRef

13.

Abu-Lebdeh, G.; Chen, H.; Benekohal, R.F.: Modeling traffic output for design of dynamic multi-cycle control in congested conditions. J. Intell. Transp. Syst. 11, 25–40 (2007). https://doi.org/10.1080/15472450601122371CrossRefMATH

14.

Gao, Y.; Liu, Y.; Hu, H.; Ge, Y.E.: Signal optimization for an isolated intersection with illegal permissive left-turning movement. Transp. B 7, 928–949 (2019). https://doi.org/10.1080/21680566.2018.1518734CrossRef

15.

Álvarez, P.; Hadi, M.A.: Evaluating the effectiveness of signal timing optimization based on microscopic simulation. Obras y Proyectos 16, 85–94 (2018)

16.

Cakici, Z.; Murat, Y.S.: A differential evolution algorithm-based traffic control model for signalized intersections. Adv. Civ. Eng. (2019). https://doi.org/10.1155/2019/7360939CrossRef

17.

Dabiri, S.; Abbas, M.: Arterial traffic signal optimization using Particle Swarm Optimization in an integrated VISSIM-MATLAB simulation environment. In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp. 766–771. IEEE (2016)

18.

Golafshani, E.M.; Behnood, A.; Arashpour, M.: Predicting the compressive strength of normal and high-performance concretes using ANN and ANFIS hybridized with grey wolf optimizer. Constr. Build. Mater. 232, 117266 (2020)CrossRef

19.

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

20.

Singh, L.; Tripathi, S.; Arora, H.: Time optimization for traffic signal control using genetic algorithm. Int. J. Recent Trends Eng. 2, 4 (2009)

21.

Li, Z.; Chen, K.: Research on timing optimization of reginal traffic signals based on improved genetic algorithm. MS&E 423, 12191 (2018)

22.

Hadi, M.A.; Wallace, C.E.: Hybrid genetic algorithm to optimize signal phasing and timing. Transportation Research Record 1421, TRB, National Research Council, Washington, DC, pp. 104–112 (1993)

23.

Park, B.; Messer, C.J.; Urbanik, T.: Enhanced genetic algorithm for signal-timing optimization of oversaturated intersections. Transp. Res. Rec. (2000). https://doi.org/10.3141/1727-05CrossRef

24.

Putha, R.; Quadrifoglio, L.; Zechman, E.: Comparing ant colony optimization and genetic algorithm approaches for solving traffic signal coordination under oversaturation conditions. Comput. Civ. Infrastruct. Eng. 27, 14–28 (2012). https://doi.org/10.1111/j.1467-8667.2010.00715.xCrossRef

25.

McCulloch, W.S.; Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5(4), 115–133 (1943)MathSciNetCrossRef

26.

Rumelhart, D.; Hinton, G.; Williams, R.: Learning internal representations by error propagation.(No. ICS-8506). California Univ San Diego la Jolla Inst for Cognitive Science (1985)

27.

Hornik, K.; Stinchcombe, M.; White, H.: Multilayer feedforward networks are universal approximators. Neural Netw 2, 359–366 (1989)CrossRef

28.

Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press (1992)

29.

Johnson, R.P.; Jain, L.C.; Vonk, E.: Automatic Generation of Neural Network Architecture using Evolutionary Computation, vol. 14. World Scientific (1997)

30.

Gmira, M.; Gendreau, M.; Lodi, A.; Potvin, J.-Y.: Travel speed prediction based on learning methods for home delivery. EURO J. Transp. Logist. 9(4), 100006 (2020)CrossRef

31.

Levenberg, K.A.: A method for the solution of certain non-linear problems in least squares. Q. Appl. Math. 2, 164–168 (1944)MathSciNetCrossRef

32.

Marquardt, D.W.: An algorithm for least-squares estimation of nonlinear parameters. J. Soc. Ind. Appl. Math. 11, 431–441 (1963). https://doi.org/10.1137/0111030MathSciNetCrossRefMATH

Title: Artificial Intelligence-Based Protocol for Macroscopic Traffic Simulation Model Development
Authors: Imran Reza
Nedal T. Ratrout
Syed M. Rahman
Publication date: 09-01-2021
Publisher: Springer Berlin Heidelberg
Published in: Arabian Journal for Science and Engineering / Issue 5/2021
Print ISSN: 2193-567X
Electronic ISSN: 2191-4281
DOI: https://doi.org/10.1007/s13369-020-05266-z

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Other articles of this Issue 5/2021

An Improved Creep Model for Granite Based on the Deviatoric Stress-to-Peak Strength Ratio

Correction to: Simple Approach for the Determination of Temperature Susceptibility Characteristics of Pure and Modified Asphalt Binders

Experimental and Numerical Studies on Pull-out Behavior of Granular Anchor Pile Foundation Embedded in Sandy Soil

Assessment of Seismic Behavior Factor of Code-Designed Steel–Concrete Composite Buildings

Flow Dynamics Around Permeable Spur Dike in a Rectangular Channel

Groundwater Contamination by Hazardous Wastes

Premium Partners