Optimal Reservation Volume of Urban Roads Based on Travel Reservation Strategy
Bibliographic record
Abstract
Improving the spatial‐temporal balance between the supply and demand of urban transportation and alleviating traffic congestion are important ways to build sustainable cities. The travel reservation strategy (TRS) is more flexible and refined than traditional traffic demand management methods. This study aims to determine the optimal reservation volume (ORV) for urban roads and verify the effectiveness of the TRS. First, we employed the sustained flow index to estimate the ORV from the degree of trade‐off between the road breakdown probability and capacity. Then, a bilevel programming model based on ORV constraints was established to analyse the effectiveness of the TRS. The results indicated that the ORV range is 0.79–0.89 times the road capacity. The TRS can achieve the best steady benefit when the demand for reservation travel reaches at least 40%. Selecting the most congested critical roads in the network to implement TRS is more effective than on a large area. The driver default behavior will increase the V/C ratios and travel costs of all roads in the network. It has been proven that the reservation transportation mode will promote the spatial‐temporal balance between supply and demand to alleviate traffic congestion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".