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When driving becomes risky : micro-scale variants of the lane-changing maneuver in highway traffic

Amna Qayyum, Bernard De Baets (UGent) , Samuel Van Ackere (UGent) , Frank Witlox (UGent) , Guy De Tré (UGent) and Nico Van de Weghe (UGent)
(2023) TRAFFIC INJURY PREVENTION. 24(7). p.583-591
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Abstract
Objective: Vehicular lane-changing is one of the riskiest driving maneuvers. Since vehicular automation is quickly becoming a reality, it is crucial to be able to identify when such a maneuver can turn into a risky situation. Recently, it has been shown that a qualitative approach: the Point Descriptor Precedence (PDP) representation, is able to do so. Therefore, this study aims to investigate whether the PDP representation can detect hazardous micro movements during lane-changing maneuvers in a situation of structural congestion in the morning and/or evening.Method: The approach involves analyzing a large real-world traffic dataset using the PDP representation and adding safety distance points to distinguish subtle movement patterns.Results: Based on these subtleties, we label four out of seven and five out of nine lane-change maneuvers as risky during the selected peak and the off-peak traffic hours respectively.Conclusions: The results show that the approach can identify risky movement patterns in traffic. The PDP representation can be used to check whether certain adjustments (e.g., changing the maximum speed) have a significant impact on the number of dangerous behaviors, which is important for improving road safety. This approach has practical applications in penalizing traffic violations, improving traffic flow, and providing valuable information for policymakers and transport experts. It can also be used to train autonomous vehicles in risky driving situations.
Keywords
Pattern recognition, road safety, spatiotemporal modeling, vehicle interactions, data mining, intelligent transportation system, PATTERNS

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MLA
Qayyum, Amna, et al. “When Driving Becomes Risky : Micro-Scale Variants of the Lane-Changing Maneuver in Highway Traffic.” TRAFFIC INJURY PREVENTION, vol. 24, no. 7, 2023, pp. 583–91, doi:10.1080/15389588.2023.2242993.
APA
Qayyum, A., De Baets, B., Van Ackere, S., Witlox, F., De Tré, G., & Van de Weghe, N. (2023). When driving becomes risky : micro-scale variants of the lane-changing maneuver in highway traffic. TRAFFIC INJURY PREVENTION, 24(7), 583–591. https://doi.org/10.1080/15389588.2023.2242993
Chicago author-date
Qayyum, Amna, Bernard De Baets, Samuel Van Ackere, Frank Witlox, Guy De Tré, and Nico Van de Weghe. 2023. “When Driving Becomes Risky : Micro-Scale Variants of the Lane-Changing Maneuver in Highway Traffic.” TRAFFIC INJURY PREVENTION 24 (7): 583–91. https://doi.org/10.1080/15389588.2023.2242993.
Chicago author-date (all authors)
Qayyum, Amna, Bernard De Baets, Samuel Van Ackere, Frank Witlox, Guy De Tré, and Nico Van de Weghe. 2023. “When Driving Becomes Risky : Micro-Scale Variants of the Lane-Changing Maneuver in Highway Traffic.” TRAFFIC INJURY PREVENTION 24 (7): 583–591. doi:10.1080/15389588.2023.2242993.
Vancouver
1.
Qayyum A, De Baets B, Van Ackere S, Witlox F, De Tré G, Van de Weghe N. When driving becomes risky : micro-scale variants of the lane-changing maneuver in highway traffic. TRAFFIC INJURY PREVENTION. 2023;24(7):583–91.
IEEE
[1]
A. Qayyum, B. De Baets, S. Van Ackere, F. Witlox, G. De Tré, and N. Van de Weghe, “When driving becomes risky : micro-scale variants of the lane-changing maneuver in highway traffic,” TRAFFIC INJURY PREVENTION, vol. 24, no. 7, pp. 583–591, 2023.
@article{01HAKQ5449374VBX0ZFM68YRJA,
  abstract     = {{Objective: Vehicular lane-changing is one of the riskiest driving maneuvers. Since vehicular automation is quickly becoming a reality, it is crucial to be able to identify when such a maneuver can turn into a risky situation. Recently, it has been shown that a qualitative approach: the Point Descriptor Precedence (PDP) representation, is able to do so. Therefore, this study aims to investigate whether the PDP representation can detect hazardous micro movements during lane-changing maneuvers in a situation of structural congestion in the morning and/or evening.Method: The approach involves analyzing a large real-world traffic dataset using the PDP representation and adding safety distance points to distinguish subtle movement patterns.Results: Based on these subtleties, we label four out of seven and five out of nine lane-change maneuvers as risky during the selected peak and the off-peak traffic hours respectively.Conclusions: The results show that the approach can identify risky movement patterns in traffic. The PDP representation can be used to check whether certain adjustments (e.g., changing the maximum speed) have a significant impact on the number of dangerous behaviors, which is important for improving road safety. This approach has practical applications in penalizing traffic violations, improving traffic flow, and providing valuable information for policymakers and transport experts. It can also be used to train autonomous vehicles in risky driving situations.}},
  author       = {{Qayyum, Amna and De Baets, Bernard and Van Ackere, Samuel and Witlox, Frank and De Tré, Guy and Van de Weghe, Nico}},
  issn         = {{1538-9588}},
  journal      = {{TRAFFIC INJURY PREVENTION}},
  keywords     = {{Pattern recognition,road safety,spatiotemporal modeling,vehicle interactions,data mining,intelligent transportation system,PATTERNS}},
  language     = {{eng}},
  number       = {{7}},
  pages        = {{583--591}},
  title        = {{When driving becomes risky : micro-scale variants of the lane-changing maneuver in highway traffic}},
  url          = {{http://doi.org/10.1080/15389588.2023.2242993}},
  volume       = {{24}},
  year         = {{2023}},
}

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