IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i17p4541-d259618.html
   My bibliography  Save this article

Global Optimality under Internet of Vehicles: Strategy to Improve Traffic Safety and Reduce Energy Dissipation

Author

Listed:
  • Jinhua Tan

    (School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China)

  • Li Gong

    (School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China)

  • Xuqian Qin

    (School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China)

Abstract

Internet of Vehicles (IoV), which enables information exchange among vehicles, infrastructures and environment, is considered to have great potential for improving traffic. However, information delays may lead to driver’s incorrect operations and have a negative impact on traffic flow. To improve traffic safety and reduce energy dissipation under IoV conditions, this paper intends to explore a more favorable driving strategy, which may weaken the adverse effects of information delays. This study regarding driving strategy is based on an improved car-following model with consideration of Global Optimality (GO-FVD model). Linear stability analysis and numerical simulations are carried out to explore the effects of Global Optimality on traffic flow. Results confirm that Global Optimality contributes to enhancing the stability and safety of traffic flow as well as depressing the energy dissipation. In particular, it is more suitable for the low-density traffic to account for Global Optimality. These results can provide theoretical support for the development of favorable driving strategy under IoV conditions, which will promote the sustainable development of intelligent transportation.

Suggested Citation

  • Jinhua Tan & Li Gong & Xuqian Qin, 2019. "Global Optimality under Internet of Vehicles: Strategy to Improve Traffic Safety and Reduce Energy Dissipation," Sustainability, MDPI, vol. 11(17), pages 1-16, August.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:17:p:4541-:d:259618
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/17/4541/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/11/17/4541/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gipps, P.G., 1981. "A behavioural car-following model for computer simulation," Transportation Research Part B: Methodological, Elsevier, vol. 15(2), pages 105-111, April.
    2. Danish Farooq & Sarbast Moslem & Szabolcs Duleba, 2019. "Evaluation of Driver Behavior Criteria for Evolution of Sustainable Traffic Safety," Sustainability, MDPI, vol. 11(11), pages 1-15, June.
    3. Sun, Yuqing & Ge, Hongxia & Cheng, Rongjun, 2018. "An extended car-following model under V2V communication environment and its delayed-feedback control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 349-358.
    4. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
    5. Knorr, Florian & Schreckenberg, Michael, 2012. "Influence of inter-vehicle communication on peak hour traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(6), pages 2225-2231.
    6. Denos C. Gazis & Robert Herman & Richard W. Rothery, 1961. "Nonlinear Follow-the-Leader Models of Traffic Flow," Operations Research, INFORMS, vol. 9(4), pages 545-567, August.
    7. Anandkumar Balasubramaniam & Anand Paul & Won-Hwa Hong & HyunCheol Seo & Jeong Hong Kim, 2017. "Comparative Analysis of Intelligent Transportation Systems for Sustainable Environment in Smart Cities," Sustainability, MDPI, vol. 9(7), pages 1-12, June.
    8. Kuang, Hua & Xu, Zhi-Peng & Li, Xing-Li & Lo, Siu-Ming, 2017. "An extended car-following model accounting for the average headway effect in intelligent transportation system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 778-787.
    9. Yang Liu & Xuedong Yan & Yun Wang & Zhuo Yang & Jiawei Wu, 2017. "Grid Mapping for Spatial Pattern Analyses of Recurrent Urban Traffic Congestion Based on Taxi GPS Sensing Data," Sustainability, MDPI, vol. 9(4), pages 1-15, March.
    10. Yi Zhang & Xiaoguang Yang & Qixing Liu & Chaoyang Li, 2015. "Who Will Use Pre-Trip Traveler Information and How Will They Respond? Insights from Zhongshan Metropolitan Area, China," Sustainability, MDPI, vol. 7(5), pages 1-18, May.
    11. G. F. Newell, 1961. "Nonlinear Effects in the Dynamics of Car Following," Operations Research, INFORMS, vol. 9(2), pages 209-229, April.
    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. Jinhua Tan & Xuqian Qin & Li Gong, 2020. "Using Vehicle-to-Vehicle Communication to Improve Traffic Safety in Sand-dust Environment," IJERPH, MDPI, vol. 17(4), pages 1-15, February.

    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. Li, Xiaopeng & Wang, Xin & Ouyang, Yanfeng, 2012. "Prediction and field validation of traffic oscillation propagation under nonlinear car-following laws," Transportation Research Part B: Methodological, Elsevier, vol. 46(3), pages 409-423.
    2. Kai Nagel & Peter Wagner & Richard Woesler, 2003. "Still Flowing: Approaches to Traffic Flow and Traffic Jam Modeling," Operations Research, INFORMS, vol. 51(5), pages 681-710, October.
    3. Tian, Junfang & Li, Guangyu & Treiber, Martin & Jiang, Rui & Jia, Ning & Ma, Shoufeng, 2016. "Cellular automaton model simulating spatiotemporal patterns, phase transitions and concave growth pattern of oscillations in traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 560-575.
    4. Dayi Qu & Shaojie Wang & Haomin Liu & Yiming Meng, 2022. "A Car-Following Model Based on Trajectory Data for Connected and Automated Vehicles to Predict Trajectory of Human-Driven Vehicles," Sustainability, MDPI, vol. 14(12), pages 1-16, June.
    5. Jiang, Rui & Hu, Mao-Bin & Zhang, H.M. & Gao, Zi-You & Jia, Bin & Wu, Qing-Song, 2015. "On some experimental features of car-following behavior and how to model them," Transportation Research Part B: Methodological, Elsevier, vol. 80(C), pages 338-354.
    6. Kun Zhang & Yu Xue & Hao-Jie Luo & Qiang Zhang & Yuan Tang & Bing-Ling Cen, 2023. "Cyber-attacks on the optimal velocity and its variation by bifurcation analyses," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(12), pages 1-19, December.
    7. Xin Chang & Xingjian Zhang & Haichao Li & Chang Wang & Zhe Liu, 2022. "A Survey on Mixed Traffic Flow Characteristics in Connected Vehicle Environments," Sustainability, MDPI, vol. 14(13), pages 1-22, June.
    8. Li, Xiaopeng & Cui, Jianxun & An, Shi & Parsafard, Mohsen, 2014. "Stop-and-go traffic analysis: Theoretical properties, environmental impacts and oscillation mitigation," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 319-339.
    9. Li, Xiangchen & Luo, Xia & He, Mengchen & Chen, Siwei, 2018. "An improved car-following model considering the influence of space gap to the response," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 536-545.
    10. Cheng, Qixiu & Liu, Zhiyuan & Lin, Yuqian & Zhou, Xuesong (Simon), 2021. "An s-shaped three-parameter (S3) traffic stream model with consistent car following relationship," Transportation Research Part B: Methodological, Elsevier, vol. 153(C), pages 246-271.
    11. Faryal Ali & Zawar Hussain Khan & Khurram Shehzad Khattak & Thomas Aaron Gulliver & Akhtar Nawaz Khan, 2022. "A Microscopic Heterogeneous Traffic Flow Model Considering Distance Headway," Mathematics, MDPI, vol. 11(1), pages 1-20, December.
    12. Tan, Jin-hua, 2019. "Impact of risk illusions on traffic flow in fog weather," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 216-222.
    13. Maosheng Li & Jing Fan & Jaeyoung Lee, 2023. "Modeling Car-Following Behavior with Different Acceptable Safety Levels," Sustainability, MDPI, vol. 15(7), pages 1-23, April.
    14. Jia, Yanfeng & Qu, Dayi & Song, Hui & Wang, Tao & Zhao, Zixu, 2022. "Car-following characteristics and model of connected autonomous vehicles based on safe potential field," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    15. Faryal Ali & Zawar Hussain Khan & Khurram Shehzad Khattak & Thomas Aaron Gulliver & Ahmed B. Altamimi, 2023. "A Microscopic Traffic Model Incorporating Vehicle Vibrations Due to Pavement Condition," Mathematics, MDPI, vol. 11(24), pages 1-24, December.
    16. Li, Xiaopeng & Ouyang, Yanfeng, 2011. "Characterization of traffic oscillation propagation under nonlinear car-following laws," Transportation Research Part B: Methodological, Elsevier, vol. 45(9), pages 1346-1361.
    17. Kalathil, Dileep & Kurzhanskiy, Alex A. & Varaiya, Pravin, 2017. "Sustainable Operation of Arterial Networks," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt5js550jt, Institute of Transportation Studies, UC Berkeley.
    18. Wang, Jinghui & Lv, Wei & Jiang, Yajuan & Qin, Shuangshuang & Li, Jiawei, 2021. "A multi-agent based cellular automata model for intersection traffic control simulation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
    19. Saifuzzaman, Mohammad & Zheng, Zuduo & Mazharul Haque, Md. & Washington, Simon, 2015. "Revisiting the Task–Capability Interface model for incorporating human factors into car-following models," Transportation Research Part B: Methodological, Elsevier, vol. 82(C), pages 1-19.
    20. Zhao, Jing & Knoop, Victor L. & Wang, Meng, 2020. "Two-dimensional vehicular movement modelling at intersections based on optimal control," Transportation Research Part B: Methodological, Elsevier, vol. 138(C), pages 1-22.

    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:17:p:4541-:d:259618. 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.