Analysis of Highway Vehicle Lane Change Duration Based on Survival Model Article Swipe
Sheng Zhao
,
Shengwen Huang
,
Huiying Wen
,
Weiming Liu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/bdcc8090114
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/bdcc8090114
To investigate highway vehicle lane-changing behavior, we utilized the publicly available naturalistic driving dataset, HighD, to extract the movement data of vehicles involved in lane changes and their proximate counterparts. We employed univariate and multivariate Cox proportional hazards models alongside random survival forest models to analyze the influence of various factors on lane change duration, assess their statistical significance, and compare the performance of multiple random survival forest models. Our findings indicate that several variables significantly impact lane change duration, including the standard deviation of lane-changing vehicles, lane-changing vehicle speed, distance to the following vehicle in the target lane, lane-changing vehicle length, and distance to the following vehicle in the current lane. Notably, the standard deviation and vehicle length act as protective factors, with increases in these variables correlating with longer lane change durations. Conversely, higher lane-changing vehicle speeds and shorter distances to following vehicles in both the current and target lanes are associated with shorter lane change durations, indicating their role as risk factors. Feature variable selection did not substantially improve the training performance of the random survival forest model based on our findings. However, validation set evaluation showed that careful feature variable selection can enhance model accuracy, leading to improved AUC values. These insights lay the groundwork for advancing research in predicting lane-changing behaviors, understanding lane-changing intentions, and developing pre-emptive safety measures against hazardous lane changes.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/bdcc8090114
- OA Status
- gold
- Cited By
- 1
- References
- 37
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4402308327Canonical identifier for this work in OpenAlex
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https://doi.org/10.3390/bdcc8090114Digital Object Identifier
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Analysis of Highway Vehicle Lane Change Duration Based on Survival ModelWork title
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articleOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
- Publication date
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2024-09-06Full publication date if available
- Authors
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Sheng Zhao, Shengwen Huang, Huiying Wen, Weiming LiuList of authors in order
- Landing page
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https://doi.org/10.3390/bdcc8090114Publisher landing page
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3390/bdcc8090114Direct OA link when available
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Random forest, Standard deviation, Duration (music), Variable (mathematics), Univariate, Computer science, Feature selection, Statistics, Multivariate statistics, Environmental science, Mathematics, Artificial intelligence, Literature, Art, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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