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Study on a multi-factor lane-changing risk resilience assessment model based on genetic algorithm and fault tree analysis

Qiang Luo et al.

Accident Analysis and Prevention2026https://doi.org/10.1016/j.aap.2026.108443article
AJG 3ABDC A*
Weight
0.46

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https://doi.org/https://doi.org/10.1016/j.aap.2026.108443

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@article{qiang2026,
  title        = {{Study on a multi-factor lane-changing risk resilience assessment model based on genetic algorithm and fault tree analysis}},
  author       = {Qiang Luo et al.},
  journal      = {Accident Analysis and Prevention},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.aap.2026.108443},
}

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Study on a multi-factor lane-changing risk resilience assessment model based on genetic algorithm and fault tree analysis

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Evidence weight

0.46

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.37 × 0.4 = 0.15
M · momentum0.60 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.