IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i23p15888-d987638.html
   My bibliography  Save this article

Implementing the Maximum Likelihood Method for Critical Gap Estimation under Heterogeneous Traffic Conditions

Author

Listed:
  • Arshad Jamal

    (Traffic and Transportation Engineering Department, College of Engineering, Imam Abdurrahman Bin Faisal University, Dammam 34212, Saudi Arabia)

  • Muhammad Ijaz

    (School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China)

  • Meshal Almosageah

    (Department of Civil Engineering, College of Engineering, Qassim University, Buraydah 52571, Saudi Arabia)

  • Hassan M. Al-Ahmadi

    (Department of Civil and Environmental Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
    Interdisciplinary Research Center of Smart Mobility and Logistics (IRC-SML), King Fahd University of Petroleum & Minerals, KFUPM, Dhahran 31261, Saudi Arabia)

  • Muhammad Zahid

    (Department of Civil, Geological, and Mining Engineering, École Polytechnique de Montréal, Montréal, QC H3T 1J4, Canada)

  • Irfan Ullah

    (School of Transportation and Logistics, Dalian University of Technology, Dalian 116024, China
    Department of Business and Administration, ILMA University, Karachi 75190, Pakistan)

  • Rabia Emhamed Al Mamlook

    (Department of Industrial Engineering and Engineering Management, Western Michigan University, Kalamazoo, MI 49008, USA
    Department of Aeronautical Engineering, University of Zawiya, Al Zawiya City P.O. Box 16418, Libya)

Abstract

Gap acceptance analysis is crucial for determining capacity and delay at uncontrolled intersections. The probability of a driver accepting an adequate gap changes over time, and in different intersection types and traffic circumstances. The majority of previous studies in this regard have assumed homogeneous traffic conditions, and applying them directly to heterogeneous traffic conditions may produce biased results. Moreover, driver behavior concerning critical gap acceptance or rejection in traffic also varies from one location to another. The current research focused on the estimation of critical gaps considering different vehicle types (cars, and two- and three-wheelers) under heterogenous traffic conditions at uncontrolled crossings in the city of Peshawar, Pakistan. A four-legged uncontrolled intersection in the study area was used to investigate drivers’ gap acceptance behavior. The gaps were investigated for various vehicle types: two-wheelers, three-wheelers, and cars. For data collection, a video recording method was used, and Avidemux video editing software was used for data investigation. The study investigated the applicability of the maximum likelihood (MLM) method to analyzing a vehicle’s critical gap. MLM estimation results indicate that the essential critical gap values for car drivers are in the range from 7.45 to 4.6 s; for two-wheelers, the critical gap was in the range from 6.78 to 4.7 s; and for three-wheelers, the values were in the range from 6.3 to 4.9 s. At an uncontrolled intersection, the proposed method’s results can assist in distinguishing between different road user groups. This study’s findings are intended to be useful to both researchers and practitioners, particularly in developing countries with similar traffic patterns and vehicle adherence patterns at unsignalized intersections.

Suggested Citation

  • Arshad Jamal & Muhammad Ijaz & Meshal Almosageah & Hassan M. Al-Ahmadi & Muhammad Zahid & Irfan Ullah & Rabia Emhamed Al Mamlook, 2022. "Implementing the Maximum Likelihood Method for Critical Gap Estimation under Heterogeneous Traffic Conditions," Sustainability, MDPI, vol. 14(23), pages 1-13, November.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:15888-:d:987638
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/23/15888/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/23/15888/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. G. Yannis & E. Papadimitriou & A. Theofilatos, 2013. "Pedestrian gap acceptance for mid-block street crossing," Transportation Planning and Technology, Taylor & Francis Journals, vol. 36(5), pages 450-462, July.
    2. Mahmassani, Hani & Sheffi, Yosef, 1981. "Using gap sequences to estimate gap acceptance functions," Transportation Research Part B: Methodological, Elsevier, vol. 15(3), pages 143-148, June.
    3. Hassan M. Al-Ahmadi & Arshad Jamal & Imran Reza & Khaled J. Assi & Syed Anees Ahmed, 2019. "Using Microscopic Simulation-Based Analysis to Model Driving Behavior: A Case Study of Khobar-Dammam in Saudi Arabia," Sustainability, MDPI, vol. 11(11), pages 1-18, May.
    4. Hagring, O., 2000. "Estimation of critical gaps in two major streams," Transportation Research Part B: Methodological, Elsevier, vol. 34(4), pages 293-313, May.
    5. Muhammad Ijaz & Lan Liu & Yahya Almarhabi & Arshad Jamal & Sheikh Muhammad Usman & Muhammad Zahid, 2022. "Temporal Instability of Factors Affecting Injury Severity in Helmet-Wearing and Non-Helmet-Wearing Motorcycle Crashes: A Random Parameter Approach with Heterogeneity in Means and Variances," IJERPH, MDPI, vol. 19(17), pages 1-24, August.
    6. Tian, Zongzhong & Vandehey, Mark & Robinson, Bruce W. & Kittelson, Wayne & Kyte, Michael & Troutbeck, Rod & Brilon, Werner & Wu, Ning, 1999. "Implementing the maximum likelihood methodology to measure a driver's critical gap," Transportation Research Part A: Policy and Practice, Elsevier, vol. 33(3-4), pages 187-197, April.
    7. Abhishek & Marko A. A. Boon & Michel Mandjes, 2019. "Generalized gap acceptance models for unsignalized intersections," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 89(3), pages 385-409, June.
    8. Arshad Jamal & Muhammad Tauhidur Rahman & Hassan M. Al-Ahmadi & Irfan Ullah & Muhammad Zahid, 2020. "Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms," Sustainability, MDPI, vol. 12(5), pages 1-23, March.
    9. R. H. Hewitt, 1983. "Measuring Critical Gap," Transportation Science, INFORMS, vol. 17(1), pages 87-109, February.
    10. Robert Ashworth, 1970. "The Analysis and Interpretation of Gap Acceptance Data," Transportation Science, INFORMS, vol. 4(3), pages 270-280, August.
    11. Tufail Ahmed & Mehdi Moeinaddini & Meshal Almoshaogeh & Arshad Jamal & Imran Nawaz & Fawaz Alharbi, 2021. "A New Pedestrian Crossing Level of Service (PCLOS) Method for Promoting Safe Pedestrian Crossing in Urban Areas," IJERPH, MDPI, vol. 18(16), pages 1-18, August.
    12. Abdulla I. M. Almadi & Rabia Emhamed Al Mamlook & Yahya Almarhabi & Irfan Ullah & Arshad Jamal & Nishantha Bandara, 2022. "A Fuzzy-Logic Approach Based on Driver Decision-Making Behavior Modeling and Simulation," Sustainability, MDPI, vol. 14(14), pages 1-19, July.
    13. Brilon, Werner & Koenig, Ralph & Troutbeck, Rod J., 1999. "Useful estimation procedures for critical gaps," Transportation Research Part A: Policy and Practice, Elsevier, vol. 33(3-4), pages 161-186, April.
    Full references (including those not matched with items on IDEAS)

    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. Tanackov, Ilija & Deretić, Nemanja & Bogdanović, Vuk & Ruškić, Nenad & Jović, Srđan, 2018. "Safety time in critical gap of left turn manoeuvre from priority approach at TWSC unsignalized intersections," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 1196-1211.
    2. Brilon, Werner & Koenig, Ralph & Troutbeck, Rod J., 1999. "Useful estimation procedures for critical gaps," Transportation Research Part A: Policy and Practice, Elsevier, vol. 33(3-4), pages 161-186, April.
    3. Bonsall, Peter & Liu, Ronghui & Young, William, 2005. "Modelling safety-related driving behaviour--impact of parameter values," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(5), pages 425-444, June.
    4. Muhammad Ijaz & Lan Liu & Yahya Almarhabi & Arshad Jamal & Sheikh Muhammad Usman & Muhammad Zahid, 2022. "Temporal Instability of Factors Affecting Injury Severity in Helmet-Wearing and Non-Helmet-Wearing Motorcycle Crashes: A Random Parameter Approach with Heterogeneity in Means and Variances," IJERPH, MDPI, vol. 19(17), pages 1-24, August.
    5. Pollatschek, Moshe A. & Polus, Abishai & Livneh, Moshe, 2002. "A decision model for gap acceptance and capacity at intersections," Transportation Research Part B: Methodological, Elsevier, vol. 36(7), pages 649-663, August.
    6. Dragan Stanimirović & Vuk Bogdanović & Slavko Davidović & Edmundas Kazimieras Zavadskas & Željko Stević, 2019. "The Influence of the Participation of Non-Resident Drivers on Roundabout Capacity," Sustainability, MDPI, vol. 11(14), pages 1-23, July.
    7. Mohammed Saleh Alfawzan & Ahmad Aftab, 2022. "Efficiency Assessment of New Signal Timing in Saudi Arabia Implementing Flashing Green Interval Complimented with Law Enforcement Cameras," Sustainability, MDPI, vol. 14(22), pages 1-15, November.
    8. Hainen, Alex, 2016. "Investigating Mixed Logit Analysis of Critical Headways at a Single-Lane Instrumented Roundabout," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 55(3), December.
    9. Khalid Almutairi & Salem Algarni & Talal Alqahtani & Hossein Moayedi & Amir Mosavi, 2022. "A TLBO-Tuned Neural Processor for Predicting Heating Load in Residential Buildings," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    10. Thanapong Champahom & Chamroeun Se & Sajjakaj Jomnonkwao & Tassana Boonyoo & Vatanavongs Ratanavaraha, 2023. "A Comparison of Contributing Factors between Young and Old Riders of Motorcycle Crash Severity on Local Roads," Sustainability, MDPI, vol. 15(3), pages 1-24, February.
    11. Wafaa Saleh & Monika Grigorova & Samia Elattar, 2020. "Pedestrian Road Crossing at Uncontrolled Mid-Block Locations: Does the Refuge Island Increase Risk?," Sustainability, MDPI, vol. 12(12), pages 1-16, June.
    12. Mark D. Manuszak & Charles F. Manski & Sanghamitra Das, 2005. "Walk or wait? An empirical analysis of street crossing decisions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(4), pages 529-548.
    13. Guilherme Henrique Alves & Geraldo Caixeta Guimarães & Fabricio Augusto Matheus Moura, 2023. "Battery Storage Systems Control Strategies with Intelligent Algorithms in Microgrids with Dynamic Pricing," Energies, MDPI, vol. 16(14), pages 1-30, July.
    14. Zhou, Hao & Toth, Christopher & Guensler, Randall & Laval, Jorge, 2022. "Hybrid modeling of lane changes near freeway diverges," Transportation Research Part B: Methodological, Elsevier, vol. 165(C), pages 1-14.
    15. Jiaming Shi & Changxu Wu & Xiuying Qian, 2020. "The Effects of Multiple Factors on Elderly Pedestrians’ Speed Perception and Stopping Distance Estimation of Approaching Vehicles," Sustainability, MDPI, vol. 12(13), pages 1-16, June.
    16. Kun Wang & Liang Xu & Han Jiang, 2022. "Analysis of the Effect of Human-Machine Co-Driving Vehicle on Pedestrian Crossing Speed at Uncontrolled Mid-Block Road Sections: A VR-Based Case Study," IJERPH, MDPI, vol. 19(12), pages 1-12, June.
    17. Awadallah, Faisal, 2009. "Intersection sight distance analysis and guidelines," Transport Policy, Elsevier, vol. 16(4), pages 143-150, August.
    18. Chen, Qun & Wang, Yan, 2015. "Cellular automata (CA) simulation of the interaction of vehicle flows and pedestrian crossings on urban low-grade uncontrolled roads," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 432(C), pages 43-57.
    19. Hagring, O., 2000. "Estimation of critical gaps in two major streams," Transportation Research Part B: Methodological, Elsevier, vol. 34(4), pages 293-313, May.
    20. Vasic, Jelena & Ruskin, Heather J., 2012. "Cellular automata simulation of traffic including cars and bicycles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(8), pages 2720-2729.

    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:23:p:15888-:d:987638. 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.