Flow Shift from Urban Roads to Beltway Expressway under a Tradable Credit Scheme to Alleviate Urban Traffic Congestion
Bibliographic record
Abstract
In recent years, severe traffic congestion in urban areas has become a growing concern for many cities in China. While beltway expressways have been built to alleviate urban congestion by diverting traffic away from city centers, existing preferential access schemes have led to overuse of certain beltway links and a decline in service levels. This research is motivated by the need to develop an improved scheme that balances urban traffic relief and beltway service level maintenance. To benefit both the government and the beltway expressway company, we propose a tradable credit scheme (TCS) to shift some of the traffic burden from urban roads to the beltway expressway while maintaining its current service level. In order to determine an appropriate TCS, a biobjective bilevel programming model is introduced. The upper‐level model seeks to maximize the revenue of the beltway expressway company while minimizing the system travel time as specified by the traffic management department. The lower‐level model describes user travel behavior under given TCS and capacity constraints of beltway expressway links, represented as the generalized capacitated traffic assignment problem. Additionally, an NSGA‐II framework is designed to discover Pareto‐optimal TCSs. Within this framework, a gradient projection method is utilized within the augmented Lagrangian multiplier algorithm to solve the lower‐level problem and evaluate the fitness of the TCS. Numerical experiments demonstrate that implementing well‐designed tradable credit schemes can effectively alleviate urban traffic congestion, reducing system travel time by up to 1.6%, while simultaneously increasing the beltway company’s revenue by up to 300% compared to the base case without requiring government subsidies. The sensitivity analysis results indicate the insufficiency of the existing preferential access or toll‐free schemes on the beltway, highlighting the greater effectiveness of the integrated strategy proposed in this study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".