IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v14y2016i1p9-d86041.html
   My bibliography  Save this article

Empirical Analysis and Modeling of Stop-Line Crossing Time and Speed at Signalized Intersections

Author

Listed:
  • Keshuang Tang

    (Department of Transportation Information and Control Engineering, College of Transportation Engineering, Tongji University, Shanghai 201804, China)

  • Fen Wang

    (School of Urban Rail Transportation, Shanghai University of Engineering Science, Shanghai 201620, China)

  • Jiarong Yao

    (Department of Transportation Information and Control Engineering, College of Transportation Engineering, Tongji University, Shanghai 201804, China)

  • Jian Sun

    (Department of Traffic Engineering, College of Transportation Engineering, Tongji University, Shanghai 201804, China
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, SiPaiLou #2, Nanjing 210096, China)

Abstract

In China, a flashing green (FG) indication of 3 s followed by a yellow (Y) indication of 3 s is commonly applied to end the green phase at signalized intersections. Stop-line crossing behavior of drivers during such a phase transition period significantly influences safety performance of signalized intersections. The objective of this study is thus to empirically analyze and model drivers’ stop-line crossing time and speed in response to the specific phase transition period of FG and Y. High-resolution trajectories for 1465 vehicles were collected at three rural high-speed intersections with a speed limit of 80 km/h and two urban intersections with a speed limit of 50 km/h in Shanghai. With the vehicle trajectory data, statistical analyses were performed to look into the general characteristics of stop-line crossing time and speed at the two types of intersections. A multinomial logit model and a multiple linear regression model were then developed to predict the stop-line crossing patterns and speeds respectively. It was found that the percentage of stop-line crossings during the Y interval is remarkably higher and the stop-line crossing time is approximately 0.7 s longer at the urban intersections, as compared with the rural intersections. In addition, approaching speed and distance to the stop-line at the onset of FG as well as area type significantly affect the percentages of stop-line crossings during the FG and Y intervals. Vehicle type and stop-line crossing pattern were found to significantly influence the stop-line crossing speed, in addition to the above factors. The red-light-running seems to occur more frequently at the large intersections with a long cycle length.

Suggested Citation

  • Keshuang Tang & Fen Wang & Jiarong Yao & Jian Sun, 2016. "Empirical Analysis and Modeling of Stop-Line Crossing Time and Speed at Signalized Intersections," IJERPH, MDPI, vol. 14(1), pages 1-13, December.
    • Handle: RePEc:gam:jijerp:v:14:y:2016:i:1:p:9-:d:86041
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/14/1/9/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/14/1/9/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Prashker, Joseph N. & Mahalel, David, 1989. "The relationship between an option space and drivers' indecision at signalized intersection approaches," Transportation Research Part B: Methodological, Elsevier, vol. 23(6), pages 401-413, December.
    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.

      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:jijerp:v:14:y:2016:i:1:p:9-:d:86041. 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.