A New Design and Environment Evaluation Approach for Managed Lanes on a Freeway Facility
Bibliographic record
Abstract
In this thesis, a new design and environmental evaluation of the managed lane is presented. HOV/HOT lane is an efficient transportation strategy aims to mitigate the congestion by tolling freeway. In the first part of this study, a dynamic toll pricing approach was taken to minimize the total passenger travel time of the tolled freeway. The model was tested using a PARAMICS microsimulation model on a section of the Deerfoot Trail in Calgary, Alberta. The environmental impact of the proposed model is determined using PARAMICS Monitor. In the second part of this study, the long-term impacts of deploying transportation strategies on greenhouse gas (GHG) emission is evaluated. While previous studies relied only on simulation results, this study uses Leontief’s input-output (I-O) model to capture the large-scale environmental impacts of transportation strategies. The I-O model was utilized to assess the impacts of improvements on the induced demand and evaluate the environmental impact of transportation strategy. The transportation strategies effects were estimated in terms of congestion reduction savings, to identify the industries that would be affected. The environmental impact in terms of changes in GHG emissions was conducted for all affected industries. A case study was also conducted on HOV/HOT lane deployment in Deerfoot Trail described in the first part. A sensitivity analysis was conducted for the level of GHG emission savings enhanced by transportation strategies for Calgary and Edmonton, which are two major Alberta cities that are similar in size, population and congestion level to compare the results. The results of the study show that the traditional approaches that focus on simply evaluating the short-term impacts of these strategies considerably overestimate the reduction of GHG emissions. Another major finding of this study is that deploying transportation strategies that would result in the same reduction in congestion levels is shown to result in significantly different long-term impacts on GHG emissions of the two examined cities. This is mainly attributed to the difference in the structure of the economic and industrial sectors in the two 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".