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Record W3159846705 · doi:10.1155/2021/5552099

Interactive Influence Analysis of Tunnel Lateral Clearance on Driving Behavior Using Expressway Field Data

2021· article· en· W3159846705 on OpenAlexvenueno aff
Li Wan, Mo Zhou, Ge Liu, Chang-An Zhang, Ying Yan

Bibliographic record

VenueJournal of Advanced Transportation · 2021
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsnot available
FundersGovernment of Shandong ProvinceKey Science and Technology Program of Shaanxi ProvinceNational Natural Science Foundation of China
KeywordsSteering wheelSimulationStandard deviationOperating speedDriving simulatorAutomotive engineeringComputer scienceEngineeringMathematicsStatistics

Abstract

fetched live from OpenAlex

Changes in lateral clearance are prone to drastic changes in the driving environment at the entrance and exit of the tunnel, which can cause a driver to become psychologically stressed and deviate from the center of a lane, thus creating a greater security risk. However, most of the existing regulations and studies only focus on the horizontal and vertical alignment of the tunnel entrances and exits, and there are few studies on the influence of lateral clearance on driving behavior. This study hired 15 random subjects to conduct real vehicle tests in eight tunnels on expressways with 3 design speeds by using a CAN-OBD analyzer and steering wheel angle meter. First, in five lateral clearance variation schemes, different speed characteristic indicators and steering wheel angles were selected as the indicators of driving behavior. Second, the interactive influence of the design speed, lateral clearance, operating speed, steering wheel angle, and other indicators were analyzed. Finally, paired t-test analysis and Wilcoxon and Friedman nonparametric tests were used to compare the differences in various indicators among different lateral clearance schemes. The results showed that when the left lateral clearance is 1.5 meters, the operating speed is increased by 3.9%, while the standard deviation of speed is small, and the driving performance is higher. When the right lateral clearance is 1.75 and 2.00 meters, the operating speed is not much different. However, the latter’s speed standard deviation is smaller. By contrast, when the right lateral clearance is up to 2.25 m, the operating speed increases by 3.7%. However, the speed standard deviation also increases. Different lateral clearances have little effect on the steering wheel angle. The operating speed on the right side is higher and more stable when the design speed is 100 km/h. This study provides scientific suggestions for the setting of the lateral clearance of the tunnels.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.280
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2021
Admission routes1
Has abstractyes

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