IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i21p5976-d280854.html
   My bibliography  Save this article

Simulation-Based Analysis of the Effect of Significant Traffic Parameters on Lane Changing for Driving Logic “Cautious” on a Freeway

Author

Listed:
  • Danish Farooq

    (Department of Transport Technology and Economics, Budapest University of Technology and Economics, Stoczek u. 2, H-1111 Budapest, Hungary)

  • Janos Juhasz

    (Department of Transport Technology and Economics, Budapest University of Technology and Economics, Stoczek u. 2, H-1111 Budapest, Hungary)

Abstract

Lane changing of traffic flow is a complicated and significant behavior for traffic safety on the road. Frequent lane changing can cause serious traffic safety issues, particularly on a two-lane road section of a freeway. This study aimed to analyze the effect of significant traffic parameters for traffic safety on lane change frequency using the studied calibrated values for driving logic “conscious” in VISSIM. Video-recorded traffic data were utilized to calibrate the model under specified traffic conditions, and the relationship between observed variables were estimated using simulation plots. The results revealed that changes in average desired speed and traffic volume had a positive relationship with lane change frequency. In addition, lane change frequency was observed to be higher when the speed distribution was set large. 3D surface plots were also developed to show the integrated effect of specified traffic parameters on lane change frequency. Results showed that high average desired speed and large desired speed distribution coupled with high traffic volume increased the lane change frequency tremendously. The study also attempted to develop a regression model to quantify the effect of the observed parameters on lane change frequency. The regression model results showed that desired speed distribution had the highest effect on lane change frequency compared to other traffic parameters. The findings of the current study highlight the most significant traffic parameters that influence the lane change frequency.

Suggested Citation

  • Danish Farooq & Janos Juhasz, 2019. "Simulation-Based Analysis of the Effect of Significant Traffic Parameters on Lane Changing for Driving Logic “Cautious” on a Freeway," Sustainability, MDPI, vol. 11(21), pages 1-15, October.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:21:p:5976-:d:280854
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/21/5976/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/21/5976/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Levy, David T & Asch, Peter, 1989. "Speeding, Coordination, and the 55-MPH Limit: Comment," American Economic Review, American Economic Association, vol. 79(4), pages 913-915, September.
    2. Li, Xin-Gang & Jia, Bin & Gao, Zi-You & Jiang, Rui, 2006. "A realistic two-lane cellular automata traffic model considering aggressive lane-changing behavior of fast vehicle," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 367(C), pages 479-486.
    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. Martin Fellendorf & Peter Vortisch, 2010. "Microscopic Traffic Flow Simulator VISSIM," International Series in Operations Research & Management Science, in: Jaume Barceló (ed.), Fundamentals of Traffic Simulation, chapter 0, pages 63-93, Springer.
    5. Gomes, Gabriel & May, Adolf & Horowitz, Roberto, 2004. "Calibration of VISSIM for a Congested Freeway," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt7bs9b2v3, Institute of Transportation Studies, UC Berkeley.
    6. Jian Wang & Jian-Xun Ding & Qin Shi & Reinhart D. Kühne, 2016. "Lane-changing behavior and its effect on energy dissipation using full velocity difference model," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 27(02), pages 1-14, February.
    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. Yakup Çelikbilek & Sarbast Moslem, 2023. "A grey multi criteria decision making application for analyzing the essential reasons of recurrent lane change," OPSEARCH, Springer;Operational Research Society of India, vol. 60(2), pages 916-941, June.
    2. Danish Farooq & Sarbast Moslem & Arshad Jamal & Farhan Muhammad Butt & Yahya Almarhabi & Rana Faisal Tufail & Meshal Almoshaogeh, 2021. "Assessment of Significant Factors Affecting Frequent Lane-Changing Related to Road Safety: An Integrated Approach of the AHP–BWM Model," IJERPH, MDPI, vol. 18(20), pages 1-17, October.
    3. Fadyushin Alexey & Zakharov Dmitrii, 2020. "Influence of the Parameters of the Bus Lane and the Bus Stop on the Delays of Private and Public Transport," Sustainability, MDPI, vol. 12(22), pages 1-18, November.
    4. Danish Farooq & Sarbast Moslem, 2022. "Estimating Driver Behavior Measures Related to Traffic Safety by Investigating 2-Dimensional Uncertain Linguistic Data—A Pythagorean Fuzzy Analytic Hierarchy Process Approach," Sustainability, MDPI, vol. 14(3), pages 1-21, February.
    5. Qiang Luo & Xiaodong Zang & Xu Cai & Huawei Gong & Jie Yuan & Junheng Yang, 2021. "Vehicle Lane-Changing Safety Pre-Warning Model under the Environment of the Vehicle Networking," Sustainability, MDPI, vol. 13(9), pages 1-16, May.
    6. Xu, Ting & Zhang, Zhishun & Wu, Xingqi & Qi, Long & Han, Yi, 2021. "Recognition of lane-changing behaviour with machine learning methods at freeway off-ramps," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).

    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. Song Fang & Linghong Shen & Jianxiao Ma & Chubo Xu, 2022. "Study on the Design of Variable Lane Demarcation in Urban Tunnels," Sustainability, MDPI, vol. 14(9), pages 1-12, May.
    2. Lv, Wei & Song, Wei-guo & Liu, Xiao-dong & Ma, Jian, 2013. "A microscopic lane changing process model for multilane traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(5), pages 1142-1152.
    3. Keeler, Theodore E., 1993. "Highway Safety, Economic Behavior, and Driving Environment," University of California Transportation Center, Working Papers qt9c27z2z1, University of California Transportation Center.
    4. Fatemeh Enayatollahi & Ahmed Osman Idris & M. A. Amiri Atashgah, 2019. "Modelling bus bunching under variable transit demand using cellular automata," Public Transport, Springer, vol. 11(2), pages 269-298, August.
    5. Peter D. Loeb & William A. Clarke, 2005. "The Determinants of Truck Accidents in the United States," Working Papers Rutgers University, Newark 2005-002, Department of Economics, Rutgers University, Newark.
    6. Qi, Le & Zheng, Zhongyi & Gang, Longhui, 2017. "A cellular automaton model for ship traffic flow in waterways," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 705-717.
    7. Lv, Wei & Song, Wei-guo & Fang, Zhi-ming & Ma, Jian, 2013. "Modelling of lane-changing behaviour integrating with merging effect before a city road bottleneck," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(20), pages 5143-5153.
    8. Daniel Shefer & Piet Rietveld, 1997. "Congestion and Safety on Highways: Towards an Analytical Model," Urban Studies, Urban Studies Journal Limited, vol. 34(4), pages 679-692, April.
    9. Abdelghany, Ahmed & Abdelghany, Khaled & Mahmassani, Hani, 2016. "A hybrid simulation-assignment modeling framework for crowd dynamics in large-scale pedestrian facilities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 86(C), pages 159-176.
    10. Xuan Fang & Hexuan Li & Tamás Tettamanti & Arno Eichberger & Martin Fellendorf, 2022. "Effects of Automated Vehicle Models at the Mixed Traffic Situation on a Motorway Scenario," Energies, MDPI, vol. 15(6), pages 1-15, March.
    11. Patrick S. McCarthy, 1991. "HIGHWAY SAFETY AND THE 65‐mph SPEED LIMIT," Contemporary Economic Policy, Western Economic Association International, vol. 9(4), pages 82-92, October.
    12. Amaro García-Suárez & José-Luis Guisado-Lizar & Fernando Diaz-del-Rio & Francisco Jiménez-Morales, 2021. "A Cellular Automata Agent-Based Hybrid Simulation Tool to Analyze the Deployment of Electric Vehicle Charging Stations," Sustainability, MDPI, vol. 13(10), pages 1-14, May.
    13. Monika Ziemska-Osuch & Dawid Osuch, 2022. "Modeling the Assessment of Intersections with Traffic Lights and the Significance Level of the Number of Pedestrians in Microsimulation Models Based on the PTV Vissim Tool," Sustainability, MDPI, vol. 14(14), pages 1-11, July.
    14. Demin Nalic & Aleksa Pandurevic & Arno Eichberger & Branko Rogic, 2020. "Design and Implementation of a Co-Simulation Framework for Testing of Automated Driving Systems," Sustainability, MDPI, vol. 12(24), pages 1-12, December.
    15. Xavier Boulet & Mahdi Zargayouna & Gérard Scemama & Fabien Leurent, 2021. "A Middleware-Based Approach for Multi-Scale Mobility Simulation," Future Internet, MDPI, vol. 13(2), pages 1-21, January.
    16. Taghreed Alghamdi & Sifatul Mostafi & Ghadeer Abdelkader & Khalid Elgazzar, 2022. "A Comparative Study on Traffic Modeling Techniques for Predicting and Simulating Traffic Behavior," Future Internet, MDPI, vol. 14(10), pages 1-21, October.
    17. Orley Ashenfelter & Michael Greenstone, 2004. "Using Mandated Speed Limits to Measure the Value of a Statistical Life," Journal of Political Economy, University of Chicago Press, vol. 112(S1), pages 226-267, February.
    18. Feng, Shumin & Li, Jinyang & Ding, Ning & Nie, Cen, 2015. "Traffic paradox on a road segment based on a cellular automaton: Impact of lane-changing behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 90-102.
    19. Li, Xin & Li, Xingang & Xiao, Yao & Jia, Bin, 2016. "Modeling mechanical restriction differences between car and heavy truck in two-lane cellular automata traffic flow model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 49-62.
    20. 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.

    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:11:y:2019:i:21:p:5976-:d:280854. 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.