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Analysis of the Insertion Angle of Lane-Changing Vehicles in Nearly Saturated Fast Road Segments

Author

Listed:
  • Quantao Yang

    (Department of Public Security and Traffic Management School, People’s Public Security University of China, Beijing 100038, China)

  • Feng Lu

    (Department of Public Security and Traffic Management School, People’s Public Security University of China, Beijing 100038, China)

  • Jingsheng Wang

    (Department of Public Security and Traffic Management School, People’s Public Security University of China, Beijing 100038, China)

  • Dan Zhao

    (Department of Public Security and Traffic Management School, People’s Public Security University of China, Beijing 100038, China)

  • Lijie Yu

    (Department of Traffic Engineering, Highway School, Chang’an University, Xi’an 710064, China)

Abstract

Vehicle lane changing in a nearly saturated fast road segment tends to increase the probability of traffic accidents in the road segment and reduce the speed of the rear vehicles in the target lane. To better analyze the relationship between the target vehicle and the front and rear vehicles in the target lane, this study focuses on the insertion angle of the target vehicle as the research object. Moreover, this study considers influencing factors, such as the longitudinal distance, transverse distance, and speed of the front and rear vehicles in the target lane. This study also adopts aerial photography to capture the flow of the main road of the Xi’an South Second Ring Road, Chang’an University segment. Information regarding the vehicle captured on video, including the speed, insertion angle, and coordinates, is extracted using the software Tracker. The coordinates correlation and speed correlation are analyzed using the software SPSS 2.0. K-means cluster analysis is applied to cluster the insertion angle of the target vehicle, and the insertion speed of the target vehicle. Of the total samples, 89.47% were inserted into the target lane at around 23° or below. The PC-Crash software was used to verify that the collision consequences gradually increased with the increase in collision angle. Therefore, when the insertion angle of the vehicle changes to lower than 23°, the overall road traffic condition is optimal, and no large losses are incurred.

Suggested Citation

  • Quantao Yang & Feng Lu & Jingsheng Wang & Dan Zhao & Lijie Yu, 2020. "Analysis of the Insertion Angle of Lane-Changing Vehicles in Nearly Saturated Fast Road Segments," Sustainability, MDPI, vol. 12(3), pages 1-17, January.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:3:p:1013-:d:314816
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    References listed on IDEAS

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    1. Denos C. Gazis, 1964. "Optimum Control of a System of Oversaturated Intersections," Operations Research, INFORMS, vol. 12(6), pages 815-831, December.
    2. Newell, G. F., 2002. "A simplified car-following theory: a lower order model," Transportation Research Part B: Methodological, Elsevier, vol. 36(3), pages 195-205, March.
    3. Lijie Yu & Quan Chen & Kuanmin Chen, 2019. "Deviation of Peak Hours for Urban Rail Transit Stations: A Case Study in Xi’an, China," Sustainability, MDPI, vol. 11(10), pages 1-21, May.
    4. Gipps, P. G., 1986. "A model for the structure of lane-changing decisions," Transportation Research Part B: Methodological, Elsevier, vol. 20(5), pages 403-414, October.
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    Cited by:

    1. Qiang Luo & Xiaodong Zang & Xu Cai & Huawei Gong & Jie Yuan & Junheng Yang, 2021. "Vehicle Lane-Changing Safety Pre-Warning Model under the Environment of the Vehicle Networking," Sustainability, MDPI, vol. 13(9), pages 1-16, May.
    2. Xu, Ting & Zhang, Zhishun & Wu, Xingqi & Qi, Long & Han, Yi, 2021. "Recognition of lane-changing behaviour with machine learning methods at freeway off-ramps," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).

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