Leveraging Location-Based Data for Assessing Network-Level Traffic Impact of Lane Management: A Case Study of Alex Fraser Bridge
Bibliographic record
Abstract
Lane management is expected to alleviate traffic congestion and improve mobility on roadways. Previous studies have mainly analyzed the impacts of lane management on the road segment rather than the road network. Because lane management strategies can affect traffic flows in the neighboring traffic regions and the entire road network, it is suitable to assess traffic impacts in the entire road network. This study proposed an analytical framework to evaluate lane management’s impacts and economic effects using location-based data, including road segments, traffic zones near road segments, and the road network. Traffic assignments with estimated origin-destination matrices from location-based data allow spatial and temporal impact analysis of lane management. This study analyzed the contraflow lane with movable median barriers installed at the Alex Fraser Bridge (AFB) in Vancouver, British Columbia, Canada, as a case study for lane management. In terms of traffic characteristics, the results showed that the contraflow lane with movable median barriers contributed significantly to improving the states of traffic flow on the AFB (traffic flow increased about 7.4%, travel speed increased about 48.3%, travel time decreased about 31.8%, and volume/capacity ratio decreased about 19.3% on average). This study showed that the contraflow lane on the AFB improved traffic flow and generated an economic benefit of $1.1 M per year (AFB, $12.7 M; zones near the AFB, −$1.4 M; Vancouver area, −$10.1 M) by estimating the changes in the value of travel time before and after lane management. This study contributes to a better understanding of using location-based data for assessing traffic impact and the economic effect of lane management operations at the network level.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".