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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
25.04.2023
DOI
https://doi.org/10.7307/ptt.v35i2.60
ISSUE
2023 (Vol 35), Issue 2
LICENSE
Copyright (c) 2024 Chuwei Zhao, Yi Zhao, Zhiqi Wang, Jianxiao Ma, Minghao Li
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Choice of Lane-Changing Point in an Urban Intertunnel Weaving Section Based on Random Forest and Support Vector Machine

Authors:

Chuwei Zhao
College of Automobile and Traffic Engineering, Nanjing Forestry University

Yi Zhao
College of Automobile and Traffic Engineering, Nanjing Forestry University

Zhiqi Wang
College of Automobile and Traffic Engineering, Nanjing Forestry University

Jianxiao Ma
College of Automobile and Traffic Engineering, Nanjing Forestry University

Minghao Li
College of Automobile and Traffic Engineering, Nanjing Forestry University

Keywords:urban intertunnel weaving section, choice of lane-changing point, random forest, support vector machine

Abstract

Urban intertunnel weaving (UIW) section is a special type of weaving section, where various lane-changing behaviours occur. To gain insight into the lane-changing behaviour in the UIW section, in this paper we attempt to analyse the decision feature and model the behaviour from the lane-changing point selection perspective. Based on field-collected lane-changing trajectory data, the lane-changing behaviours are divided into four types. Random forest method is applied to analyse the influencing factors of choice of lane-changing point. Moreover, a support vector machine model is adopted to perform decision behaviour modelling. Results reveal that there are significant differences in the influencing factors for different lane-changing types and different positions in the UIW segment. The three most important factor types are object vehicle status, current-lane rear vehicle status and target-lane rear vehicle status. The precision of the choice of lane-changing point models is at least 82%. The proposed method could reveal the detailed features of the lane-changing point selection behaviour in the UIW section and also provide a feasible choice of lane-changing point model.

References

  1. [1] Ali Y, et al. The impact of the connected environment on driving behaviour and safety: A driving simulator study. Accident Analysis and Prevention. 2020;144. DOI: 10.1016/j.aap.2020.105643.
  2. [2] Zhu J, Tasic I, Qu X. Flow-level coordination of connected and autonomous vehicles in multilane freeway ramp merging areas. Multimodal Transportation. 2022;1(1). DOI: 10.1016/j.multra.2022.100005.
  3. [3] Ouyang P, et al. Traffic safety analysis of inter-tunnel weaving section with conflict prediction models. Journal of Transportation Safety & Security. 2022;14(4):630-654. DOI: 10.1080/19439962.2020.1801924.
  4. [4] Peng B, 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+70. DOI: 10.16097/j.cnki.1009-6744.2020.04.007.
  5. [5] Duan K, et al. Analysis of driver’s decision distance and merging distance in work zone area based on parametric survival models: With the aid of a driving simulator experiment. Transportation Research Part F - Traffic Psychology and Behaviour. 2020;71:31-48. DOI: 10.1016/j.trf.2020.03.017.
  6. [6] Moridpour S, et al. Lane-changing decision model for heavy vehicle drivers. Journal of Intelligent Transportation Systems. 2012;16(1):24-35. DOI: 10.1080/15472450.2012.639640.
  7. [7] Jin H, et al. Gauss mixture hidden Markov model to characterise and model discretionary lane-change behaviours for autonomous vehicles. IET Intelligent Transport Systems. 2020;14(5):401-411. DOI: 10.1049/iet-its.2019.0446.
  8. [8] Li L, et al. Retrieving common discretionary lane changing characteristics from trajectories. IEEE Transactions on Vehicular Technology. 2018;67(3):2014-2024. DOI: 10.1109/TVT.2017.2771144.
  9. [9] Van Beinum A, et al. Driving behaviour at motorway ramps and weaving segments based on empirical trajectory data. Transportation Research Part C - Emerging Technologies. 2018;92:426-441. DOI: 10.1016/j.trc.2018.05.018.
  10. [10] Das A, Khan MN, Ahmed MM. Detecting lane change maneuvers using SHRP2 naturalistic driving data: A comparative study machine learning techniques. Accident Analysis and Prevention. 2020;142. DOI: 10.1016/j.aap.2020.105578.
  11. [11] Balal E, Cheu RL. Comparative evaluation of fuzzy inference system, support vector machine and multilayer feed-forward neural network in making discretionary lane changing decisions. Neural Network World. 2018;28(4):361-378. DOI: 10.14311/NNW.2018.28.021.
  12. [12] Feng H, Deng J, Ge T. Multi-attributes lane-changing decision model based on entropy weight with driving styles. Journal of Transportation Systems Engineering and Information Technology. 2020;20(02):139-144. DOI: 10.16097/j.cnki.1009-6744.2020.02.021.
  13. [13] Huo D, Ma J, Chang R. Lane-changing-decision characteristics and the allocation of visual attention of drivers with an angry driving style. Transportation Research Part F - Traffic Psychology and Behaviour. 2020;71:62-75. DOI: 10.1016/j.trf.2020.03.008.
  14. [14] Hang J, et al. Exploring the effects of the location of the lane-end sign and traffic volume on multistage lanechanging behaviours in work zone areas: A driving simulator-based study. Transportation Research Part F - Traffic Psychology and Behaviour. 2018;58:980-993. DOI: 10.1016/j.trf.2018.07.024.
  15. [15] Gan X, et al. Spatial-temporal varying coefficient model for lane-changing behaviour in work zone merging areas. Journal of Transportation Safety & Security. 2020;14(6):949-972. DOI: 10.1080/19439962.2020.1864075.
  16. [16] Pan TL, et al. Modeling the impacts of mandatory and discretionary lane-changing maneuvers. Transportation Research Part C - Emerging Technologies. 2016;68:403-424. DOI: 10.1016/j.trc.2016.05.002.
  17. [17] Deng S. et al. A dynamic variety model to describe the traffic flow of target lane affected by lane change on urban road. Advances in Mechanical Engineering. 2019;11(4). DOI: 10.1177/1687814019844066.
  18. [18] Toledo T, Koutsopoulos HN, Ben-Akiva ME. Modeling integrated lane-changing behaviour. Transportation Research Record Journal of the Transportation Research Board. 2003;1857(1): 30-38. DOI: 10.3141/1857-04.
  19. [19] Nilsson P, Laine L, Jacobson B. A simulator study comparing characteristics of manual and automated driving during lane changes of long combination vehicles. IEEE Transactions on Intelligent Transportation Systems. 2017;18(9):2514-2524. DOI: 10.1109/TITS.2017.2664890.
  20. [20] Ross V, et al. Investigating the influence of working memory capacity when driving behaviour is combined with cognitive load: An LCT study of young novice drivers. Accident Analysis and Prevention. 2014;62:377-387. DOI: 10.1016/j.aap.2013.06.032.
  21. [21] Antin JF, et al. Investigating lane change behaviours and difficulties for senior drivers using naturalistic driving data. Journal of Safety Research. 2020;74:81-87. DOI: 10.1016/j.jsr.2020.04.008.
  22. [22] Peng J, et al. Multi-parameter prediction of drivers’ lane-changing behaviour with neural network model. Applied Ergonomics. 2015;50:207-217. DOI: 10.1016/j.apergo.2015.03.017.
  23. [23] Yang D, et al. A dynamic lane-changing trajectory planning model for automated vehicles. Transportation Research Part C - Emerging Technologies. 2018;95:228-247. DOI: 10.1016/j.trc.2018.06.007.
  24. [24] Wang H, Xu S, Deng L. Automatic lane-changing decision based on single-step dynamic game with incomplete information and collision-free path planning. Actuators. 2021;10(8). DOI: 10.3390/act10080173.
  25. [25] Zheng O. Developing a Traffic Safety Diagnostics System for Unmanned Aerial Vehicles Using Deep Learning Algorithms. PhD thesis. University of Central Florida; 2019.
  26. [26] Zhao Y, et al. Trajectory-based characteristic analysis and decision modeling of the lane-changing process in intertunnel weaving sections. Plos One. 2022;17(4). DOI: 10.1371/journal.pone.0266489.
  27. [27] Deng J, Feng H. A multilane cellular automaton multi-attribute lane-changing decision model. Physica A - Statistical Mechanics and its Applications. 2019;529. DOI: 10.1016/j.physa.2019.121545.
  28. [28] Pang M, et al. A probability lane-changing model considering memory effect and driver heterogeneity. Transportmetrica B - Transport Dynamics. 2020;8(1):72-89. DOI: 10.1080/21680566.2020.1715310.
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How to Cite
Zhao, C. (et al.) 2023. Choice of Lane-Changing Point in an Urban Intertunnel Weaving Section Based on Random Forest and Support Vector Machine. Traffic&Transportation Journal. 35, 2 (Apr. 2023), 161-174. DOI: https://doi.org/10.7307/ptt.v35i2.60.

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