Bibliographic record
Abstract
Fairness is an important concept in transportation system because transport services are generally recognized as public goods and affect travellers’ access to basic needs. Moreover, transportation system has significant impacts on all facets of communities and these impacts may be significantly different for travellers from different groups. Thus, there is a need for considering fairness in transportation system planning. The thesis proposes methodologies to evaluate fairness impacts and design fair transportation systems. A mathematical model is developed to evaluate the fairness impacts of congestion pricing accompanied with revenue neutral mechanism and transit network re-design. The revenue-neutral mechanism uses the revenue from congestion pricing to finance public transit. This model refines existing models for cost structure of transit and road networks to predict the effects of congestion pricing policies on travel times and travel costs. The mode shift from cars to public transit is also incorporated by a choice mode. The proposed mode is implemented on Sydney Central Business District to evaluate the fairness impacts of congestion pricing policies. The evaluation results reveal that congestion pricing policies benefit transit-dependent and low-income groups and reduce the performance gap between cars and public transit. Hence, congestion pricing policies are fair if the revenue is refunded and the transit network is improved. The evaluation also suggests to assign the pricing revenue to different groups for improving public acceptability. In the context of congestion pricing revenue assignment problem. An investigation into the properties of fairness schemes is conducted. Four revenue assignment mechanisms corresponding to opportunity fairness, individual value proportional fairness, marginal value proportional fairness, and market fairness are examined to demonstrate their properties and highlight their policy implications. Then, these assignment mechanisms are applied on Winnipeg’s downtown network. The results illustrate that market fairness is the most axiomatically restrictive and is complex to compute, while opportunity fairness is the least restrictive but requires the minimal computational resources. The results also reveal that individual value proportional fairness and marginal value proportional fairness are equivalent to market fairness when the coalition effects are zero. Apart from the theoretical comparison, the lower bounds for the assignment mechanisms are characterized and a paradoxical situation where a market fairness or marginal value proportional fair assignment involves taxing a certain group is demonstrated. Finally, a model to seek the fair and efficiency subsidy schemes in the oligopolistic transit system is proposed. The model incorporates a market competition model to represent the competition between transit operators. It also includes a discrete choice model to account travellers’ mode choice behaviour. The numerical analysis illustrates the capability of the proposed model and presents empirical evidence of the trade-off between efficiency and fairness in transit system. The core contribution of the thesis is to provide insights into the properties of fairness in transportation system and to develop modelling tools to promote transportation system fairness.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".