A new design and evaluation approach for managed lanes from a sustainability perspective
Bibliographic record
Abstract
This article presents a comprehensive framework that optimizes traffic management measures to reduce emissions and fuel consumption and evaluates their operating and secondary environmental impacts. A new managed lane strategy is presented that minimizes the total passenger travel time on the freeway. This managed lane focuses on simultaneously optimizing toll schemes on the high occupancy toll (HOT) lane and the operation of the general purpose (GP) lanes. With reduced congestion and thus reduced number of acceleration and deceleration events associated with stop-and-go traffic, fuel efficiency increases and emissions are reduced. PARAMICS microscopic traffic simulator, which considers the behavior of individual vehicles (e.g., acceleration, deceleration, and lane changing behavior) is used to collect traffic performance and emission data for estimating mobility and emissions measures (i.e., operating phase). While previous studies only focused on operating phase impact, this study uses a Leontief input-output (I-O) model to establish the financial flow between industries to capture the large-scale environmental impacts of HOT lane deployment (i.e., secondary impact). The core of the new evaluation approach lies in its capability to provide a more thorough assessment of the environmental impacts of traffic management schemes by quantifying the impacts associated with the interplay between the activities of various sectors and the transportation industry. The I-O model is utilized to assess the indirect impacts of induced demand generated from network improvements and evaluate the environmental impacts of HOT lane deployment in regional economies. The developed approach is applied to The City of Calgary. The results of the study show that the traditional approaches that only evaluate the operating phase impacts of transportation strategies considerably overestimate the reduction of greenhouse gas (GHG) emissions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".