Let's Connect
Follow Us
Watch Us
(+385) 1 2380 262
journal.prometfpz.unizg.hr
Promet - Traffic&Transportation journal

Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

Sign In or Register Submit

PDF
PUBLISHED
31.10.2024
DOI
https://doi.org/10.7307/ptt.v36i5.535
ISSUE
2024 (Vol 36), Issue 5
LICENSE
Copyright (c) 2024 Minghao LI, Yi ZHAO, Jianxiao MA, Yuxin CHEN, Shuo HUAI
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

A Study on the Impact of Overtaking Lane-Changing Behaviour in Expressway Interchange Weaving Areas

Authors:Minghao LI, Yi ZHAO, Jianxiao MA, Yuxin CHEN, Shuo HUAI

Abstract

This study investigates the overtaking lane-changing (OLC) behaviour in expressway interchange weaving areas, aiming to analyse these behaviours’ causes and potential impacts. Field data are utilised to analyse the statistical characteristics of lane-changing points and spatio-temporal utilisation in weaving areas. A modified NS model, which considers the distribution pattern of vehicle speeds, and a rigid lane-changing rule based on Gaussian distribution are proposed. Additionally, a cellular automaton simulation model is constructed to quantify the influence of OLC behaviour on traffic efficiency and spatio-temporal utilisation based on simulated data. The findings indicate that the imbalanced distribution of lane-changing points and spatio-temporal utilisation in weaving segments, caused by rigid lane-changing behaviour, is an objective factor that triggers OLC behaviour. When the traffic volume in weaving areas ranges from 500 to 1,100 pcu/5 min and the proportion of OLC behaviour is between 0.35 and 0.7, the behaviour will significantly enhance the average vehicle speeds of the outermost lane of the main road and normal rigid lane-changing (NRLC) vehicles, with increases of up to 48% and 51%, respectively. Moreover, OLC behaviour also improves the balance of spatio-temporal utilisation in weaving areas and reduces the average spatio-temporal utilisation. This study clarifies the positive impact of OLC behaviour on expressway interchange weaving areas and provides new research ideas for enhancing the efficiency of these areas.

Keywords:interchange weaving areas, overtaking lane-changing behaviour, lane-changing points, spatio-temporal utilisation, cellular automaton simulation model

References

  1. [1] Peng B, Wang Y, Xie J, et al. Multi-stage lane changing decision model of urban trunk road's short weaving area based on cellular automata. Journal of Transportation Systems Engineering and Information Technology. 2020;20(04):41–48. DOI: 10.16097/j.cnki.1009-6744.2020.04.007.
  2. [2] Sun J, Jiang J, Zheng J. Improved speed prediction models on weaving segments of urban expressway. China Journal of Highway and Transport. 2017;30(01):83–91. DOI: 10.19721/j.cnki.1001-7372.2017.01.011.
  3. [3] Huang Y, Chen F, Song M, et al. Effect evaluation of traffic guidance in urban underground road diverging and merging areas: A simulator study. Accident Analysis & Prevention. 2023;186:107036. DOI: 10.1016/j.aap.2023.107036.
  4. [4] Ma Y, Meng H, Chen S, et al. Predicting traffic conflicts for expressway diverging areas using vehicle trajectory data. Journal of Transportation Engineering Part A Systems. 2020;146(3):04020003. DOI: 10.1061/JTEPBS.0000320.
  5. [5] Pulugurtha SS, Imran MS. Average travel time, planning time index, and buffer time index thresholds for freeway weaving sections, merging areas, and diverging areas. Journal of Transportation Engineering, Part A. Systems. 2021;(9):147. DOI: 10.1061/JTEPBS.0000549.
  6. [6] Guo Y, Lee C, Ma J,et al. Evaluating operational efficiency of split merge, diverge, and weaving solutions for reducing freeway bottleneck congestion. Informa UK Limited, 2021;(4). DOI: 10.1080/19427867.2020.1725260.
  7. [7] Liu X, Ko H, Guo M, et al. A new traffic model on compulsive lane-changing caused by off-ramp. Chinese Physics B, 2016;(4):7. DOI: 10.1088/1674-1056/25/4/048901.
  8. [8] Chen X, Han X, Jia X, et al. Capacity modeling of weaving area on urban expressways with exclusive bus lanes based on gap acceptance theory. Transportation Research Record: Journal of the Transportation Research Board. 2019;2673(8):365–376. DOI: 10.1177/0361198119843235.
  9. [9] Ouyang P, Wu J, Xu C, et al. Traffic safety analysis of inter-tunnel weaving section with conflict prediction models. Journal of Transportation Safety & Security. 2020;1-25. DOI: 10.1080/19439962.2020.1801924.
  10. [10] Bham, G. A simple lane change model for microscopic traffic flow simulation in weaving sections. Transportation Letters. 2011;3(4)231–251. DOI: 10.3328/TL.2011.03.04.231-251.
  11. [11] Pan J, Shen Y .Assessing driving risk at the second phase of overtaking on two-lane highways for young novice drivers based on driving simulation. International Journal of Environmental Research and Public Health. 2022;19(5). DOI: 10.3390/ijerph19052691.
  12. [12] Choudhari T, Budhkar A, Maji A. Modeling overtaking distance and time along two-lane undivided rural highways in mixed traffic condition. Transportation letters. 2022;14(2):75–83. DOI: 10.1080/19427867.2020.1815142.
  13. [13] Mahmud SMS, Ferreira L, Hoque MS, et al. Overtaking risk modeling in two-lane two-way highway with heterogeneous traffic environment of a low-income country using naturalistic driving dataset. Journal of Safety Research. 2022;80:380–390. DOI: 10.1016/j.jsr.2021.12.019.
  14. [14] Lamouik I, Yahyaouy A, Sabri MA. Model predictive control for full autonomous vehicle overtaking. Transportation Research Record. 2023;03611981221141432. DOI: 10.1177/03611981221141432.
  15. [15] Dutta B, Chakroborty P, Vasudevan V. Study of overtaking maneuvers on wide one-way roads in weak lane-disciplined traffic using naturalistic driving data. Transportation Research Record, 2023;2677(3):565–582. DOI: 10.1177/03611981221116365.
  16. [16] Moridpour S, Sarvi M, Rose G. Lane changing models: A critical review. Transportation letters, 2010;2(3):157–173. DOI: 10.3328/TL.2010.02.03.157-173.
  17. [17] Li G. Merging model in freeway weaving section based on gradient boosting decision tree. Journal of Southeast University (Natural Science Edition). 2018;48(03):563–567. DOI: 10.3969/j.issn.1001-0505.2018.03.027.
  18. [18] Arman MA, Tampère CMJ. Lane-level trajectory reconstruction based on data-fusion. Transportation Research Part C: Emerging Technologies. 2022;145:103906. DOI: 10.1016/j.trc.2022.103906.
  19. [19] Deng S, Ye X, Chen J. A model to describe the influence of the traffic flow on the main road due to off-street parking at the section of access. Journal of Harbin Institute of Technology. 2016;48(03):101–107. DOI: 10.11918/j.issn.0367-6234.2016.03.017.
  20. [20] Wang Q, Xie N, Hou D. Effects of adaptive cruise control and cooperative adaptive cruise control on traffic flow. China Journal of Highway and Transport. 2019;32(06):188–197+205. DOI: CNKI:SUN:ZGGL.0.2019-06-020.
  21. [21] Baikejuli M, Shi J. A cellular automata model for car–truck heterogeneous traffic flow considering drivers’ risky driving behaviours. International Journal of Modern Physics C, 2023;2350154. DOI: 10.1142/S0129183123501541.
  22. [22] Yang L, Huang Z, Kuang A. Influence of lane-changing rules on two−lane traffic flow of freeway. Journal of Central South University (Science and Technology). 2016;47(05):1752–1759. DOI: 10.11817/j.issn.1672-7207.2016.05.039.
  23. [23] Fan Y, Li M, Ran J, et al. Study on the influence of self-driving vehicles on mixed traffic flow of expressway. Journal of Wuhan University of Technology (Transportation Science & Engineering). 2022;46(01):28–32.
  24. [24] Wang J, Zhang Z, Lu G. A Bayesian inference based adaptive lane change prediction model. Transportation Research Part C: Emerging Technologies. 2021;132:103363. DOI: 10.1016/j.trc.2021.103363.
  25. [25] Chen K, Knoop VL, Liu P, et al. Modeling the impact of lane-changing’s anticipation on car-following behaviour. Transportation research. Part C, Emerging technologies. 2023;150:104110. DOI: 10.1016/j.trc.2023.104110.
  26. [26] Zhao CW, Zhao Y, Wang ZQ, et al. Choice of lane-changing point in an urban intertunnel weaving section based on random forest and support vector machine. Promet-Traffic & Transportation. 2023;35(2):161–174. DOI: 10.7307/ptt.v35i2.60.
  27. [27] Xie J, Peng B, Qin Y. Cellular automata model of multi-lane weaving area based on lane-changing probability distribution. Journal of Transportation Systems Engineering and Information Technology. 2021;132:103363. DOI: 10.16097/j.cnki.1009-6744.2022.03.031.
Show more

SPECIAL ISSUE CALL

Innovation and New Technologies in Transport and Logistics

Guest Editor: Eleonora Papadimitriou, PhD

Editors: Dario Babić, PhD; Marko Matulin, PhD; Marko Ševrović, PhD.

RECENT PAPERS

Browse through the selection of our recent research
Factors Influencing Sustained Use of Shared E-Scooter Services in Urban TaiwanA Deep Learning Approach for Enhanced Real-Time Prediction of Winter Road Surface Temperatures in High-Altitude Mountain AreasImplications of Retrograde Behaviours on Visual and Cycling Behaviour of Normal CyclistsCollege Students’ Cognition and Attitude Towards Connected and Autonomous Vehicles in China: an Exploratory Study

Accelerating Discoveries in Traffic Science |
2024 © Promet - Traffic&Transportation journal