IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i17p13081-d1229060.html
   My bibliography  Save this article

Calculation Method of Deceleration Lane Length and Slope Based on Reliability Theory

Author

Listed:
  • Xin Tian

    (Highway Academy, Chang’an University, Xi’an 710064, China)

  • Mengmeng Shi

    (Highway Academy, Chang’an University, Xi’an 710064, China)

  • Mengyu Shao

    (Highway Academy, Chang’an University, Xi’an 710064, China)

  • Binghong Pan

    (Highway Academy, Chang’an University, Xi’an 710064, China)

Abstract

The deceleration lane is an important part of the freeway, and the rationality of its design parameters affects the exit accident rate. The traditional calculation method is based on the design of speed and vehicle parameters using deterministic methods, ignoring the randomness of the driver’s deceleration behavior. It is necessary to calculate the length and slope of the deceleration lane in detail according to the deceleration characteristics of the driver in the deceleration section by using the uncertainty method. This paper describes a study on the maximum longitudinal slope of the downhill section of the deceleration lane, where the safety of diverging vehicles is unfavorable. By collecting deceleration lane data from interchanges around Xi’an (Shaanxi Province, China, Coordinates: 108.95, 34.27) and analyzing the deceleration characteristics of vehicles, we propose a new deceleration model. In addition, the limit-state functions of the length and slope of the deceleration lane have been established based on the reliability theory. Finally, according to the deceleration characteristics, we determined the probability distribution of key parameters in the vehicle deceleration process. We used the Monte Carlo Simulation (MCS) and the Improved First-Order Second Moments (IFOSM) calculation model to calculate the length and slope of the deceleration lane, respectively. Finally, we propose the recommended values for the length and slope of the deceleration lane. The results of the study showed that: (1) The movement process of the vehicle on the deceleration section adopts a uniform deceleration, and the truck and the car start to decelerate from the starting of the taper section and diverging point, respectively. (2) The control vehicle in the deceleration lane calculation model is the compact car. (3) The reliability theory has good applicability in calculating freeway alignment indexes. It fully considers the probability of driver deceleration behavior in the calculation model, which provides a more suitable method for the calculation of deceleration lane indexes.

Suggested Citation

  • Xin Tian & Mengmeng Shi & Mengyu Shao & Binghong Pan, 2023. "Calculation Method of Deceleration Lane Length and Slope Based on Reliability Theory," Sustainability, MDPI, vol. 15(17), pages 1-26, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:13081-:d:1229060
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/17/13081/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/17/13081/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hyeonseo Kim & Kyeongjoo Kwon & Nuri Park & Juneyoung Park & Mohamed Abdel-Aty, 2021. "Crash- and Simulation-Based Safety Performance Evaluation of Freeway Rest Area," Sustainability, MDPI, vol. 13(9), pages 1-13, April.
    2. Ronghua Wang & Jiangbi Hu & Xiaoqin Zhang, 2016. "Analysis of the Driver’s Behavior Characteristics in Low Volume Freeway Interchange," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-9, April.
    3. Kayvan Aghabayk & Majid Sarvi & William Young, 2015. "A State-of-the-Art Review of Car-Following Models with Particular Considerations of Heavy Vehicles," Transport Reviews, Taylor & Francis Journals, vol. 35(1), pages 82-105, January.
    4. Rong-xia Xia & De-hua Wu & Jie He & Ya Liu & Deng-feng Shi, 2016. "A New Model of Stopping Sight Distance of Curve Braking Based on Vehicle Dynamics," Discrete Dynamics in Nature and Society, Hindawi, vol. 2016, pages 1-8, October.
    5. Cihe Chen & Zijian Lin & Shuguang Zhang & Feng Chen & Peiyan Chen & Lin Zhang, 2021. "The Compatibility between the Takeover Process in Conditional Automated Driving and the Current Geometric Design of the Deceleration Lane in Highway," Sustainability, MDPI, vol. 13(23), pages 1-17, December.
    6. Federico Orsini & Mariaelena Tagliabue & Giulia De Cet & Massimiliano Gastaldi & Riccardo Rossi, 2021. "Highway Deceleration Lane Safety: Effects of Real-Time Coaching Programs on Driving Behavior," Sustainability, MDPI, vol. 13(16), pages 1-16, August.
    7. Junhyung Lee, 2022. "Acceleration and Deceleration Rates in Interrupted Flow Based on Empirical Digital Tachograph Data," Sustainability, MDPI, vol. 14(18), pages 1-16, September.
    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. Shuaiyang Jiao & Shengrui Zhang & Bei Zhou & Zixuan Zhang & Liyuan Xue, 2020. "An Extended Car-Following Model Considering the Drivers’ Characteristics under a V2V Communication Environment," Sustainability, MDPI, vol. 12(4), pages 1-18, February.
    2. Junyan Han & Xiaoyuan Wang & Gang Wang, 2022. "Modeling the Car-Following Behavior with Consideration of Driver, Vehicle, and Environment Factors: A Historical Review," Sustainability, MDPI, vol. 14(13), pages 1-27, July.
    3. Wang, Pengcheng & Yu, Guizhen & Wu, Xinkai & Qin, Hongmao & Wang, Yunpeng, 2018. "An extended car-following model to describe connected traffic dynamics under cyberattacks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 351-370.
    4. Yong Fang & Jiayi Zhou & Hua Hu & Yanxi Hao & Dianliang Xiao & Shaojie Li, 2022. "Combination Layout of Traffic Signs and Markings of Expressway Tunnel Entrance Sections: A Driving Simulator Study," Sustainability, MDPI, vol. 14(6), pages 1-13, March.
    5. Giovanni Pau & Tiziana Campisi & Antonino Canale & Alessandro Severino & Mario Collotta & Giovanni Tesoriere, 2018. "Smart Pedestrian Crossing Management at Traffic Light Junctions through a Fuzzy-Based Approach," Future Internet, MDPI, vol. 10(2), pages 1-19, February.
    6. Maria Rodionova & Angi Skhvediani & Tatiana Kudryavtseva, 2022. "Prediction of Crash Severity as a Way of Road Safety Improvement: The Case of Saint Petersburg, Russia," Sustainability, MDPI, vol. 14(16), pages 1-20, August.

    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:17:p:13081-:d:1229060. 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.