IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v604y2022ics037843712200574x.html
   My bibliography  Save this article

A comparative study on measurement of lane-changing trajectory similarities

Author

Listed:
  • Hamedi, Hamidreza
  • Shad, Rouzbeh
  • Ziaee, Seyed Ali

Abstract

Lane change (LC) represents an important driving behavior and significantly influences traffic efficiency and safety. Spatial LC behavior should be evaluated for vehicles in a transportation system, identifying vehicle movement patterns through similarities in LC trajectories. Contexts determine the trajectory of an objective. Therefore, it is necessary to deeply understand these contexts so that they could be incorporated into movement investigation. Both internal and external contexts pose direct/indirect impacts on movements and enable movement changes. As a result, contexts should be treated as distinct movement process dimensions, and the recording of data for trajectory evaluation based on spatiotemporal dimensions is inevitable. However, due to complicated inter-dimension associations, trajectory similarity measurement has rarely been studied in the literature. This paper focuses on contextualizing a similarity measure of LC trajectories using internal and external contexts and spatial footprints. A total of three models, including the longest common subsequence (LCSS), edit distance on real sequences (EDR), and dynamic time warping (DTW), are employed to examine multi-dimensional similarities between trajectories. The Next Generation Simulation (NGSIM) data were applied to evaluate these models. Contextual data were observed to be crucial parameters capable of increasing and decreasing movements. The similarities were found to be dependent on the thresholds in the EDR and LCSS models, and a change in the thresholds changed the outcome. To the best of the author’s knowledge, this paper is the first research on the use of several models for the similarity evaluation of LC trajectories.

Suggested Citation

  • Hamedi, Hamidreza & Shad, Rouzbeh & Ziaee, Seyed Ali, 2022. "A comparative study on measurement of lane-changing trajectory similarities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    • Handle: RePEc:eee:phsmap:v:604:y:2022:i:c:s037843712200574x
    DOI: 10.1016/j.physa.2022.127895
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037843712200574X
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2022.127895?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Tang, Jinjun & Bi, Wei & Liu, Fang & Zhang, Wenhui, 2021. "Exploring urban travel patterns using density-based clustering with multi-attributes from large-scaled vehicle trajectories," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
    2. Chen, Tianyi & Shi, Xiupeng & Wong, Yiik Diew, 2021. "A lane-changing risk profile analysis method based on time-series clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    3. Coifman, Benjamin & Li, Lizhe, 2017. "A critical evaluation of the Next Generation Simulation (NGSIM) vehicle trajectory dataset," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 362-377.
    4. He, Zhengbing & Zheng, Liang & Guan, Wei, 2015. "A simple nonparametric car-following model driven by field data," Transportation Research Part B: Methodological, Elsevier, vol. 80(C), pages 185-201.
    5. Lie Guo & Ping-Shu Ge & Ming Yue & Yi-Bing Zhao, 2014. "Lane Changing Trajectory Planning and Tracking Controller Design for Intelligent Vehicle Running on Curved Road," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-9, January.
    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. Yao, Handong & Li, Qianwen & Li, Xiaopeng, 2020. "A study of relationships in traffic oscillation features based on field experiments," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 339-355.
    2. Yang, Yichen & Cao, Tianyu & Xu, Shangzhi & Qian, Yeqing & Li, Zhipeng, 2022. "Influence of driving style on traffic flow fuel consumption and emissions based on the field data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    3. Ronan Keane & H. Oliver Gao, 2021. "Fast Calibration of Car-Following Models to Trajectory Data Using the Adjoint Method," Transportation Science, INFORMS, vol. 55(3), pages 592-615, May.
    4. Kou, Yukang & Ma, Changxi, 2023. "Dual-objective intelligent vehicle lane changing trajectory planning based on polynomial optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 617(C).
    5. Wang, Xiao & Jiang, Rui & Li, Li & Lin, Yi-Lun & Wang, Fei-Yue, 2019. "Long memory is important: A test study on deep-learning based car-following model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 786-795.
    6. 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.
    7. Cheng-Ju Song & Hong-Fei Jia, 2022. "Car-Following Model Optimization and Simulation Based on Cooperative Adaptive Cruise Control," Sustainability, MDPI, vol. 14(21), pages 1-12, October.
    8. He, Jia & He, Zhengbing & Fan, Bo & Chen, Yanyan, 2020. "Optimal location of lane-changing warning point in a two-lane road considering different traffic flows," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    9. Hossain, Md. Anowar & Tanimoto, Jun, 2022. "A microscopic traffic flow model for sharing information from a vehicle to vehicle by considering system time delay effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    10. Dong, Shuoxuan & Zhou, Yang & Chen, Tianyi & Li, Shen & Gao, Qiantong & Ran, Bin, 2021. "An integrated Empirical Mode Decomposition and Butterworth filter based vehicle trajectory reconstruction method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    11. Tian, Junfang & Li, Guangyu & Treiber, Martin & Jiang, Rui & Jia, Ning & Ma, Shoufeng, 2016. "Cellular automaton model simulating spatiotemporal patterns, phase transitions and concave growth pattern of oscillations in traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 560-575.
    12. Sun, Jie & Zheng, Zuduo & Sun, Jian, 2020. "The relationship between car following string instability and traffic oscillations in finite-sized platoons and its use in easing congestion via connected and automated vehicles with IDM based control," Transportation Research Part B: Methodological, Elsevier, vol. 142(C), pages 58-83.
    13. Tianjun Feng & Keyi Liu & Chunyan Liang, 2023. "An Improved Cellular Automata Traffic Flow Model Considering Driving Styles," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    14. Dongjun Kim & Jinsung Yun & Kijung Kim & Seungil Lee, 2021. "A Comparative Study of the Robustness and Resilience of Retail Areas in Seoul, Korea before and after the COVID-19 Outbreak, Using Big Data," Sustainability, MDPI, vol. 13(6), pages 1-21, March.
    15. Han, Yu & Zhang, Mingyu & Guo, Yanyong & Zhang, Le, 2022. "A streaming-data-driven method for freeway traffic state estimation using probe vehicle trajectory data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    16. Dhari Ali Mahmood & Gábor Horváth, 2020. "Analysis of the Message Propagation Speed in VANET with Disconnected RSUs," Mathematics, MDPI, vol. 8(5), pages 1-21, May.
    17. Yu, Yuewen & Luo, Xia & Su, Qiming & Peng, Weikang, 2023. "A dynamic lane-changing decision and trajectory planning model of autonomous vehicles under mixed autonomous vehicle and human-driven vehicle environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    18. Xing, Yang & Lv, Chen & Cao, Dongpu & Lu, Chao, 2020. "Energy oriented driving behavior analysis and personalized prediction of vehicle states with joint time series modeling," Applied Energy, Elsevier, vol. 261(C).
    19. Xu, Yueru & Zheng, Yuan & Yang, Ying, 2021. "On the movement simulations of electric vehicles: A behavioral model-based approach," Applied Energy, Elsevier, vol. 283(C).
    20. Siqueira, Adriano F. & Peixoto, Carlos J.T. & Wu, Chen & Qian, Wei-Liang, 2016. "Effect of stochastic transition in the fundamental diagram of traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 87(C), pages 1-13.

    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:eee:phsmap:v:604:y:2022:i:c:s037843712200574x. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.