IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i18p11422-d912765.html
   My bibliography  Save this article

Optimized Intersection Signal Timing: An Intelligent Approach-Based Study for Sustainable Models

Author

Listed:
  • Hong Ki An

    (Department of Transportation Engineering, Huaiyin Institute of Technology, Huai’an 223003, China)

  • Muhammad Awais Javeed

    (School of Information Engineering, Chang’an University, Xi’an 710064, China)

  • Gimok Bae

    (Division of Smart City Engineering, School of Civil and Environmental Engineering, Daejin University, Pocheon 11159, Korea)

  • Nimra Zubair

    (School of Information Engineering, Chang’an University, Xi’an 710064, China)

  • Ahmed Sayed M. Metwally

    (Department of Mathematics, College of Science, King Saud University, Riyadh 11451, Saudi Arabia)

  • Patrizia Bocchetta

    (Dipartimento di Ingegneria dell’Innovazione, Università del Salento, Via Monteroni, 73100 Lecce, Italy)

  • Fan Na

    (School of Information Engineering, Chang’an University, Xi’an 710064, China)

  • Muhammad Sufyan Javed

    (School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China)

Abstract

With the development of intelligent transportation systems, research into intelligent traffic signal control has received considerable attention. To date, many traffic signal control models have been studied, where most of the models concentrate on how to minimize travel time, vehicle delay, and the number of stops or how to maximize capacity. This study introduces the Garra Rufa–inspired (GRI) algorithm, which is used to optimize traffic signal control modelling considering the number of vehicles in a queue. GRI has the characteristics of using the decision variables of the code as the operation object, directly using the objective function value for the search information, using multiple search points at the same time, and using probability search technology. Theoretical analysis of intelligent optimization and research into application methods were carried out to resolve the problem of traffic signal optimization control. The output of the GRI algorithm was compared, calibrated, and validated with SIDRA. Furthermore, to obtain more comprehensive results, the genetic algorithm (GA) and particle swarm optimization (PSO) were also compared. The results of the analysis show that the GRI decreases by 10.1% (intersection A) and 16.5% (intersection B) in the number of vehicles in the queue.

Suggested Citation

  • Hong Ki An & Muhammad Awais Javeed & Gimok Bae & Nimra Zubair & Ahmed Sayed M. Metwally & Patrizia Bocchetta & Fan Na & Muhammad Sufyan Javed, 2022. "Optimized Intersection Signal Timing: An Intelligent Approach-Based Study for Sustainable Models," Sustainability, MDPI, vol. 14(18), pages 1-19, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11422-:d:912765
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/18/11422/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/18/11422/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. E. XEVI & K. Christiaens & A. Espino & W. Sewnandan & D. Mallants & H. Sørensen & J. Feyen, 1997. "Calibration, Validation and Sensitivity Analysis of the MIKE-SHE Model Using the Neuenkirchen Catchment as Case Study," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 11(3), pages 219-242, June.
    2. Dewees, Donald N, 1979. "Estimating the Time Costs of Highway Congestion," Econometrica, Econometric Society, vol. 47(6), pages 1499-1512, November.
    3. Guo, Jianhua & Kong, Ye & Li, Zongzhi & Huang, Wei & Cao, Jinde & Wei, Yun, 2019. "A model and genetic algorithm for area-wide intersection signal optimization under user equilibrium traffic," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 92-104.
    4. Bastiaan Possel & Luc J. J. Wismans & Eric C. Berkum & Michiel C. J. Bliemer, 2018. "The multi-objective network design problem using minimizing externalities as objectives: comparison of a genetic algorithm and simulated annealing framework," Transportation, Springer, vol. 45(2), pages 545-572, March.
    5. Camagni, Roberto & Gibelli, Maria Cristina & Rigamonti, Paolo, 2002. "Urban mobility and urban form: the social and environmental costs of different patterns of urban expansion," Ecological Economics, Elsevier, vol. 40(2), pages 199-216, February.
    6. Ceylan, Halim & Bell, Michael G. H., 2004. "Traffic signal timing optimisation based on genetic algorithm approach, including drivers' routing," Transportation Research Part B: Methodological, Elsevier, vol. 38(4), pages 329-342, May.
    7. Goran Vuk & Christian Hansen, 2006. "Validating the Passenger Traffic Model for Copenhagen," Transportation, Springer, vol. 33(4), pages 371-392, July.
    8. Hymel, Kent, 2019. "If you build it, they will drive: Measuring induced demand for vehicle travel in urban areas," Transport Policy, Elsevier, vol. 76(C), pages 57-66.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Riyadh Kamil Chillab & Aqeel S. Jaber & Mouna Ben Smida & Anis Sakly, 2023. "Optimal DG Location and Sizing to Minimize Losses and Improve Voltage Profile Using Garra Rufa Optimization," Sustainability, MDPI, vol. 15(2), pages 1-13, January.
    2. Zahra Zeinaly & Mahdi Sojoodi & Sadegh Bolouki, 2023. "A Resilient Intelligent Traffic Signal Control Scheme for Accident Scenario at Intersections via Deep Reinforcement Learning," Sustainability, MDPI, vol. 15(2), pages 1-26, January.
    3. Suhaib Alshayeb & Aleksandar Stevanovic & Nikola Mitrovic & Elio Espino, 2022. "Traffic Signal Optimization to Improve Sustainability: A Literature Review," Energies, MDPI, vol. 15(22), pages 1-24, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Arnott, Richard & Inci, Eren, 2010. "The stability of downtown parking and traffic congestion," Journal of Urban Economics, Elsevier, vol. 68(3), pages 260-276, November.
    2. Danjie Shen & Shujing Dong, 2022. "Transition of Urban Morphology in the Mountainous Areas Since Early-Modern Times from the Perspective of Urban Historic Landscape—A GIS Tools and Historical Map Translation Approach," Sustainability, MDPI, vol. 14(19), pages 1-21, October.
    3. Miquel-Àngel Garcia-López & Ilias Pasidis & Elisabet Viladecans-Marsal, 2022. "Congestion in highways when tolls and railroads matter: evidence from European cities [The congestion relief benefit of public transit: evidence from Rome]," Journal of Economic Geography, Oxford University Press, vol. 22(5), pages 931-960.
    4. Assel Nugmanova & Wulf-Holger Arndt & Md Aslam Hossain & Jong Ryeol Kim, 2019. "Effectiveness of Ring Roads in Reducing Traffic Congestion in Cities for Long Run: Big Almaty Ring Road Case Study," Sustainability, MDPI, vol. 11(18), pages 1-26, September.
    5. Bo Liu & Desheng Xue & Yiming Tan, 2019. "Deciphering the Manufacturing Production Space in Global City-Regions of Developing Countries—a Case of Pearl River Delta, China," Sustainability, MDPI, vol. 11(23), pages 1-26, December.
    6. Joseph Y. J. Chow & Amelia C. Regan, 2011. "Real Option Pricing of Network Design Investments," Transportation Science, INFORMS, vol. 45(1), pages 50-63, February.
    7. Katrina Raynor & Severine Mayere & Tony Matthews, 2018. "Do ‘city shapers’ really support urban consolidation? The case of Brisbane, Australia," Urban Studies, Urban Studies Journal Limited, vol. 55(5), pages 1056-1075, April.
    8. Yan Yan & Hui Liu & Ningcheng Wang & Shenjun Yao, 2021. "How Does Low-Density Urbanization Reduce the Financial Sustainability of Chinese Cities? A Debt Perspective," Land, MDPI, vol. 10(9), pages 1-18, September.
    9. Ali Enes Dingil & Federico Rupi & Domokos Esztergár-Kiss, 2021. "An Integrative Review of Socio-Technical Factors Influencing Travel Decision-Making and Urban Transport Performance," Sustainability, MDPI, vol. 13(18), pages 1-20, September.
    10. Souche, Stéphanie, 2009. "Un exemple d’estimation de la demande de transport urbain," Revue d'économie régionale et urbaine, Editions NecPlus, vol. 2009(04), pages 759-779, December.
    11. Rogier Pennings & Bart Wiegmans & Tejo Spit, 2020. "Can We Have Our Cake and Still Eat It? A Review of Flexibility in the Structural Spatial Development and Passenger Transport Relation in Developing Countries," Sustainability, MDPI, vol. 12(15), pages 1-25, July.
    12. Salvati, Luca & Sateriano, Adele & Grigoriadis, Efstathios & Carlucci, Margherita, 2017. "New wine in old bottles: The (changing) socioeconomic attributes of sprawl during building boom and stagnation," Ecological Economics, Elsevier, vol. 131(C), pages 361-372.
    13. Junhua Chen & Shufan Ma & Na Liu, 2023. "Multi-dimensional Housing Inequality Index: The Provincial Evidence from China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 165(2), pages 633-654, January.
    14. Safirova, Elena & Gillingham, Kenneth & Houde, Sébastien, 2007. "Measuring marginal congestion costs of urban transportation: Do networks matter?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(8), pages 734-749, October.
    15. Ozan, Cenk & Haldenbilen, Soner & Ceylan, Halim, 2011. "Estimating emissions on vehicular traffic based on projected energy and transport demand on rural roads: Policies for reducing air pollutant emissions and energy consumption," Energy Policy, Elsevier, vol. 39(5), pages 2542-2549, May.
    16. Przemysław Śleszyński & Adam Kowalewski & Tadeusz Markowski & Paulina Legutko-Kobus & Maciej Nowak, 2020. "The Contemporary Economic Costs of Spatial Chaos: Evidence from Poland," Land, MDPI, vol. 9(7), pages 1-28, July.
    17. Lenzen, Manfred & Dey, Christopher & Foran, Barney, 2004. "Energy requirements of Sydney households," Ecological Economics, Elsevier, vol. 49(3), pages 375-399, July.
    18. Tian, Guangjin & Jiang, Jing & Yang, Zhifeng & Zhang, Yaoqi, 2011. "The urban growth, size distribution and spatio-temporal dynamic pattern of the Yangtze River Delta megalopolitan region, China," Ecological Modelling, Elsevier, vol. 222(3), pages 865-878.
    19. Yu Song & Guofan Shao & Xiaodong Song & Yong Liu & Lei Pan & Hong Ye, 2017. "The Relationships between Urban Form and Urban Commuting: An Empirical Study in China," Sustainability, MDPI, vol. 9(7), pages 1-17, July.
    20. Haldenbilen, Soner, 2006. "Fuel price determination in transportation sector using predicted energy and transport demand," Energy Policy, Elsevier, vol. 34(17), pages 3078-3086, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11422-:d:912765. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.