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A lane-changing risk profile analysis method based on time-series clustering

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  • Chen, Tianyi
  • Shi, Xiupeng
  • Wong, Yiik Diew

Abstract

Lane-changing (LC) is an essential driving maneuver on roadways, and risky LC maneuvers account for a large number of crash accidents. This study investigates the LC risk profile during an LC process. A risk indicator based on driving safety field theory is employed to measure the instantaneous LC risk at each timestamp during an LC process and generate the LC risk profile. Then, Dynamic Time Warping (DTW) k-means clustering, as a time-series clustering method, is applied to partition the LC risk profiles into several categories. The Next Generation Simulation (NGSIM) US-101 dataset, which contains detailed records of vehicles’ trajectories, is used for case study. In the case study, the LC risk profiles are categorized into “uphill” shape, “bell” shape, and “downhill” shape. The LC risk profiles with “uphill” shape account for the majority of the LC risk profiles. Besides, we find that the LC process with “uphill” shaped risk profile generally has higher LC risk, and the crash risk between LC car and its preceding cars are more relevant to the LC risk. Those findings are likely due to the LC maneuver with the purpose to overtake the preceding car in the original lane. The risk indicator based on driving safety field theory can measure LC risk more comprehensively, compared to the conventional surrogate measures. The DTW k-means clustering method offers a promising approach to investigate the causation of risky LC maneuver based on the risk profile during an LC process.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:565:y:2021:i:c:s0378437120308657
    DOI: 10.1016/j.physa.2020.125567
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    References listed on IDEAS

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    Cited by:

    1. 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).
    2. Giuseppe Ciaburro & Gino Iannace, 2021. "Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review," Data, MDPI, vol. 6(6), pages 1-30, May.
    3. 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).
    4. 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.
    5. Bo Wang & Chi Zhang & Yiik Diew Wong & Lei Hou & Min Zhang & Yujie Xiang, 2022. "Comparing Resampling Algorithms and Classifiers for Modeling Traffic Risk Prediction," IJERPH, MDPI, vol. 19(20), pages 1-23, October.

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