Electric vehicle drivers’ choices of expressway usage and peak avoidance: an empirical analysis considering the random effects among individuals
Bibliographic record
Abstract
Traffic congestion is a persistent challenge in urban areas, particularly with increased private car ownership post-COVID-19. Traditional administrative measures to manage traffic demand have proven unsustainable, necessitating more effective strategies. This study examines trajectory data from 3,064 commuting (CMT) and 2,690 non-commuting (Non-CMT) electric vehicles in Shanghai to analyze how travel purposes, distances, directions, and departure times influence expressway usage and peak avoidance decisions. It identifies significant differences in choices between CMT and Non-CMT users, highlighting the need for personalized travel management methods based on vehicle usage patterns. Route management strategies should focus on commuting trips for CMT vehicle users, while spatial control measures considering the trip distance and direction can reduce Non-CMT vehicle users’ dependence on expressways during peak hours. This research contributes to enhancing our understanding of the heterogeneity of travelers’ behaviors and offers personal and practical insights for managing traffic congestion sustainably in metropolitan cities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".