IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i24p16923-d1301947.html
   My bibliography  Save this article

Limited Response of Curve Safety Level to Friction Factor and Superelevation Variation under Repeated Traffic Loads

Author

Listed:
  • Jinliang Xu

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

  • Miao Jia

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

  • Chao Gao

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

  • Wenzhen Lv

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

Abstract

Although road horizontal curves are high-risk sections for accidents, current road safety assessments often neglect the dynamic evolution of superelevation and the friction factor. The connotation for road safety level was clarified by examining the significance of road factors in traffic safety through the systemic characteristics of roads. Among these characteristics, curve safety level is determined by the ratio of the supply and demand of the lateral friction factor. On the basis of international standards and specifications, this study clarified the design supply and demand of friction factors for curve by considering the distribution of tangential and lateral friction factors. Expanding on the steady-state bicycle model while accounting for road geometric parameters and vehicle operation characteristics, the lateral friction factor demanded for vehicles was quantified. Meanwhile, the characteristics of the friction factor supplied and the superelevation variation were analyzed by using the road service life as a variable, along with their influence on the actual supply of the friction factor and the curve safety level. The results of the analysis indicate a rapid decrease in curve safety level during the first two years of road utilization, followed by a slower declining trend, with a significant 27% reduction in curve safety level by the end of the second year. Furthermore, the decline in the curve safety level is mainly attributed to variations in the road surface friction factor, whereas the influence of superelevation variation on the curve safety level is restricted. In the absence of maintenance interventions, the curve safety level will decrease by over 30% after three years of operation. Controlling operational speed is one of the effective measures for ensuring traffic safety. Meanwhile, the impact of the friction factor and the superelevation variation on the curve safety level accumulates over time, thus causing drivers to have difficulty perceiving these alterations. Therefore, dynamic safety evaluations that account for the fluctuation in the friction factor and superelevation induced by repetitive vehicle loading must be undertaken.

Suggested Citation

  • Jinliang Xu & Miao Jia & Chao Gao & Wenzhen Lv, 2023. "Limited Response of Curve Safety Level to Friction Factor and Superelevation Variation under Repeated Traffic Loads," Sustainability, MDPI, vol. 15(24), pages 1-17, December.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:24:p:16923-:d:1301947
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/24/16923/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/24/16923/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yanna Yin & Huiying Wen & Lu Sun & Wei Hou, 2020. "The Influence of Road Geometry on Vehicle Rollover and Skidding," IJERPH, MDPI, vol. 17(5), pages 1-17, March.
    2. Lord, Dominique & Mannering, Fred, 2010. "The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(5), pages 291-305, June.
    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. Najaf, Pooya & Thill, Jean-Claude & Zhang, Wenjia & Fields, Milton Greg, 2018. "City-level urban form and traffic safety: A structural equation modeling analysis of direct and indirect effects," Journal of Transport Geography, Elsevier, vol. 69(C), pages 257-270.
    2. Buddhavarapu, Prasad & Bansal, Prateek & Prozzi, Jorge A., 2021. "A new spatial count data model with time-varying parameters," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 566-586.
    3. Bo Yang & Yao Wu & Weihua Zhang & Jie Bao, 2020. "Modeling Collision Probability on Freeway: Accounting for Different Types and Severities in Various LOS," Sustainability, MDPI, vol. 12(18), pages 1-13, September.
    4. Dong, Chunjiao & Shao, Chunfu & Clarke, David B. & Nambisan, Shashi S., 2018. "An innovative approach for traffic crash estimation and prediction on accommodating unobserved heterogeneities," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 407-428.
    5. Lv, Jinpeng & Lord, Dominique & Zhang, Yunlong & Chen, Zhi, 2015. "Investigating Peltzman effects in adopting mandatory seat belt laws in the US: Evidence from non-occupant fatalities," Transport Policy, Elsevier, vol. 44(C), pages 58-64.
    6. Ye, Wei & Xu, Yueru & Shi, Xiaomeng & Shiwakoti, Nirajan & Ye, Zhirui & Zheng, Yuan, 2024. "A macroscopic safety indicator for road segment: application of entropy theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 642(C).
    7. Ruru Xing & Zimu Li & Xiaoyu Cai & Zepeng Yang & Ningning Zhang & Tao Yang, 2023. "Accident Rate Prediction Model for Urban Expressway Underwater Tunnel," Sustainability, MDPI, vol. 15(13), pages 1-28, July.
    8. Ulak, Mehmet Baran & Ozguven, Eren Erman & Spainhour, Lisa & Vanli, Omer Arda, 2017. "Spatial investigation of aging-involved crashes: A GIS-based case study in Northwest Florida," Journal of Transport Geography, Elsevier, vol. 58(C), pages 71-91.
    9. Amir Saman Abdollahzadeh Nasiri & Omid Rahmani & Ali Abdi Kordani & Nader Karballaeezadeh & Amir Mosavi, 2020. "Evaluation of Safety in Horizontal Curves of Roads Using a Multi-Body Dynamic Simulation Process," IJERPH, MDPI, vol. 17(16), pages 1-20, August.
    10. Milhan Moomen & Amirarsalan Mehrara Molan & Khaled Ksaibati, 2023. "A Random Parameters Multinomial Logit Model Analysis of Median Barrier Crash Injury Severity on Wyoming Interstates," Sustainability, MDPI, vol. 15(14), pages 1-17, July.
    11. Yan, Ying & Zhang, Ying & Yang, Xiangli & Hu, Jin & Tang, Jinjun & Guo, Zhongyin, 2020. "Crash prediction based on random effect negative binomial model considering data heterogeneity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    12. Hwachyi Wang & S. K. Jason Chang & Hans De Backer & Dirk Lauwers & Philippe De Maeyer, 2019. "Integrating Spatial and Temporal Approaches for Explaining Bicycle Crashes in High-Risk Areas in Antwerp (Belgium)," Sustainability, MDPI, vol. 11(13), pages 1-28, July.
    13. Sun, Chenshuo & Pei, Xin & Hao, Junheng & Wang, Yewen & Zhang, Zuo & Wong, S.C., 2018. "Role of road network features in the evaluation of incident impacts on urban traffic mobility," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 101-116.
    14. Qingzhou Wang & Yaxuan Zhao & Lujia Li & Liying Kong & Wenjing Si, 2024. "Influence of Snowy and Icy Weather on Vehicle Sideslip and Rollover: A Simulation Approach," Sustainability, MDPI, vol. 16(2), pages 1-30, January.
    15. Buckley, Cathal & Howley, Peter & Jordan, Phil, . "The role of differing farming motivations on the adoption of nutrient management practices," International Journal of Agricultural Management, Institute of Agricultural Management, vol. 4(4).
    16. Kojiro Matsuo & Kosuke Miyazaki & Nao Sugiki, 2022. "A Method for Locational Risk Estimation of Vehicle–Children Accidents Considering Children’s Travel Purposes," IJERPH, MDPI, vol. 19(21), pages 1-16, October.
    17. Hana Naghawi, 2018. "Negative Binomial Regression Model for Road Crash Severity Prediction," Modern Applied Science, Canadian Center of Science and Education, vol. 12(4), pages 1-38, April.
    18. Rose Luke, 2023. "Current and Future Trends in Driver Behaviour and Traffic Safety Scholarship: An African Research Agenda," IJERPH, MDPI, vol. 20(5), pages 1-23, February.
    19. Guilong Xu & Jinliang Xu & Chao Gao & Rishuang Sun & Huagang Shan & Yongji Ma & Jinsong Ran, 2022. "A Novel Safety Assessment Framework for Pavement Friction Evolution Due to Traffic on Horizontal Curves," Sustainability, MDPI, vol. 14(17), pages 1-14, August.
    20. Chen Chen & Feng Guo, 2016. "Evaluating the influence of crashes on driving risk using recurrent event models and Naturalistic Driving Study data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(12), pages 2225-2238, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:15:y:2023:i:24:p:16923-:d:1301947. 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.